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lab
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Laboratory Phonology
Journal of the Association for
Laboratory Phonology
Ennever, T. et al 2017 A replicable acoustic measure of lenition and the
nature of variability in Gurindji stops. Laboratory Phonology: Journal
of the Association for Laboratory Phonology 8(1): 20, pp. 1–32, DOI:
https://doi.org/10.5334/labphon.18
JOURNAL ARTICLE
A replicable acoustic measure of lenition and the
nature of variability in Gurindji stops
Thomas Ennever1,2, Felicity Meakins1,2 and Erich R. Round1,2
1
University of Queensland, St Lucia, QLD 4072, AU
2
ARC Centre of Excellence for the Dynamics of Language, AU
Corresponding author: Erich R. Round (e.round@uq.edu.au)
An automated method is presented for the commensurable, reproducible measurement of
duration and lenition of segment types ranging from fully occluded stops to highly lenited
variants, in acoustic data. The method is motivated with respect to the relationship between
acoustic and articulatory phonetics and, through subsequent evaluation, is argued to correspond
well to articulation. It is then applied to the phonemic stops of casual speech in Gurindji
(Pama-Nyungan, Australia) to investigate the nature of their articulatory targets. The degree of
stop lenition is found to vary widely. Contrary to expectations, no evidence is found of a positive
effect on lenition due to word-medial (relative to word-initial) position, beyond that attributable
to duration; nor do non-coronals lenite more than their apical counterparts, which freely lenite
along a continuum towards taps. No significant effect is found of preceding or following vocalic
environment. Taken together, the observed lenition, duration, and peak intensity velocities
are argued to be inconsistent with a single, fully-occluded articulatory ‘stop’ target which is
undershot at short durations, rather targets can be understood to span a range or ‘window’ of
values in the sense of Keating (1990), from fully-occluded stop-like targets to more approximantlike targets. It is an open question to what degree the patterns found in Gurindji are language
particular, or can be related to the organization of obstruent systems in Australian languages
more broadly. Precisely comparable studies of additional languages will be especially valuable
in addressing these questions and others, and are possible using the method we introduce.
Keywords: Lenition; stops; articulatory phonology; acoustic phonetics; intensity; Gurindji;
Australian languages
1. Introduction
The phonemic obstruent systems of Australian languages are systems of contrasting
extremes. In one dimension, they host an abundance of place of articulation contrasts,
particularly in the coronal region, and these are increasingly well understood (Anderson
& Maddieson, 1994; Bundgaard-Nielsen et al., 2012, 2015; Butcher, 1995; Proctor
et al., 2010; Tabain & Butcher, 2015; Tabain & Rickard, 2007). In all other dimensions,
they are impoverished: Most possess just a single obstruent series, with no contrast in
laryngeal features, length, or between stops and fricatives (Busby, 1980; Evans, 1995).
Nevertheless, allophonic stop lenition patterns are widely reported in descriptions of
Australian languages, and raise the question of exactly how the parametric space of
‘manner of articulation’ is utilized within Australian languages. The investigation of such
matters bears on theories that propose language-specific influences on gestural target
setting (Keating, 1990; Guenther, 1995).
An open research question in Articulatory Phonology and Task Dynamic approaches is
whether gestural targets are to be construed as single points (Saltzman & Munhall, 1989)
Art. 20, page 2 of 32
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
or as ‘windows’ or ‘ranges’ of targets (Keating, 1990). To this end, we are interested in
whether the lenition of obstruents in one Australian language can be explained as i)
the mechanical byproduct of temporal reduction causing undershoot relative to a single,
point-like target, ii) due to other known factors effecting stop-lenition in a similar manner, or iii) due to speakers actively selecting among multiple available target articulations
within a range or window.
In order to answer these questions using acoustic data we present a novel method for
deterministically and automatically demarcating phonemic stops and their allophonic
variants, and deriving quantitative measures of lenition using intensity data. Detailing
this method, and assessing it, comprise a major contribution of the paper.
We then proceed to a fine-grained acoustic-phonetic study of the realizations of
single-series phonemic obstruents in an Australian language with respect to manner of
articulation and lenition, with particular attention to the synchronic phonetic variability of phonemic obstruents in casual speech. We investigate phonemic stops in Gurindji
(Ngumpin-Yapa subgroup of Pama-Nyungan) and ask the following questions:
1. What is the range of realizations (in terms of lenition) of the phonemic stops
in Gurindji, and their relative frequencies?
2. Are these influenced by a stop’s place of articulation, vocalic environment,
and/or word boundary adjacency, and if so, how?
3. Is there evidence to support an analysis of Gurindji stop phonemes having a
single, fully-occluded point-like articulatory target, with more lenited variants the product of undershoot due to short duration; or conversely, is there
evidence for a window-like range of articulatory targets?
To answer these questions, we study intervocalic realizations of four Gurindji phonemic
stops /p t ʈ k/ in the casual speech of a female speaker. The paper is organized as follows.
Section 1 provides a background to Australian obstruents, common patterns of allophony,
and establishes known factors affecting stop lenition. We also survey the challenges posed
by gradient phonetic variation and the need for robust techniques for the analysis of
casual speech. Section 2 introduces the materials used in the study. In Section 3 we introduce and evaluate an automated procedure for delimiting, in a commensurable manner,
stop-like and approximant-like segments from acoustic, casual speech data and estimating
their degree of lenition. This research tool is applied to the Gurindji data in Section 4.
Results for factors affecting lenition are presented in Section 5. Implications for the types
of articulatory targets underlying phonemic stops in Gurindji are discussed in Section 6.
Section 7 concludes.
1.1. Gurindji
Gurindji is a Ngumpin-Yapa (Pama-Nyungan) language spoken in the Victoria River
District of the Northern Territory, Australia. It is the traditional language of the Gurindji
people who live in the communities of Kalkaringi and Daguragu (Meakins et al., 2013). It
is currently endangered with approximately 40 speakers remaining. Younger generations
now speak the mixed language Gurindji Kriol (McConvell & Meakins, 2005).
1.1.1. Phoneme inventory
Gurindji’s phonological inventory is typical of many Pama-Nyungan languages, comprising a five-way place of articulation distinction for obstruents and corresponding nasals,
three laterals, three glides, and a tap/trill, shown in Table 1. Gurindji makes no contrasts in terms of voicing, consonant length, or frication, and accordingly obstruents
are transcribed in Table 1 using the conventional voiceless IPA symbols. Phonetically,
the pre-palatal obstruent /c/ is realized consistently as an affricate by the speaker we
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 3 of 32
study (Ennever, 2014b) and so is excluded from the present study. Like many Australian
languages, the vowel system of Gurindji is sparse, contrasting three qualities and length
(Meakins et al., 2013), shown in Table 2.
1.1.2. Morphological and prosodic structure
Primary stress falls on the initial syllable of Gurindji words without exception. The stress
system has not been studied in detail, though broadly speaking, it resembles those of
many Pama-Nyungan languages, with secondary stress on most suffix-initial syllables and
alternating stress otherwise (Dixon, 2002, p. 557). A consequence is that word-initial syllables fall at the left boundaries of both the morphosyntactic word and a prosodic word.
In this study, we examine intervocalic stop phonemes in word initial position (i.e., flanked
on the left by the final vowel of a preceding word), and in morpheme-medial position.
These two positions contrast in terms of (non-)adjacency to both morphosyntactic and
phonological word boundaries. Given the state of knowledge of Gurindji’s stress system,
we make no specific claims about foot boundaries, other than to note that word-initial
tokens will always be foot-initial also.
1.2. Phonemic obstruents in Australian languages
Australian languages are known for their rich place of articulation distinctions, particularly among coronals—languages contrast either one or two apical articulations, plus one
or two laminal articulations (Busby, 1980). Gurindji follows the double-apical pattern,
contrasting apical alveolar and apical retroflex articulations, in addition to a single laminal
pre-palatal place and the non-coronals; a bilabial and a dorso-velar. Cross-linguistically in
Australia, alveolar phonemes vary in their precise point of contact with the alveolar ridge
and retroflexes vary in terms of posterior placement and sublaminal contact (Chadwick,
1975; McGregor, 1990; Tabain, 2009). Even in languages that contrast two apical places,
the contrast is typically neutralized word initially (Butcher, 1995; Tabain & Butcher,
2015; Steriade, 2001). This is true also in Gurindji.
Australian languages are also known for their paucity of manner distinctions, particularly
among obstruents (Butcher, 2006). Only a handful of Australian languages possess phonemically contrastive fricatives, or stops that contrast in phonation or length (Butcher, 2004;
B. Evans & Merlan, 2004; Evans, 1995, p. 730; McKay, 1980; Stoakes et al., 2007). Gurindji
is typical in this sense, lacking any laryngeal, length, or manner contrast among obstruents.
1.2.1. Synchronic allophony
Allophonically, stops in Australian languages are commonly reported to possess lenited
variants when flanked by vowels and/or liquids (Dixon, 2002; Evans, 1995). Non-coronal
Table 1: Gurindji consonant phonemes after Meakins et al. (2013). Orthography is in parentheses.
Stop
Nasal
Lateral
Tap/Trill
Glide
Bilabial
Alveolar
Retroflex
Pre-palatal
Velar
p (p)
m (m)
t (t)
n (n)
l (l)
r (rr)
ʈ (rt)
ɳ (rn)
ɭ (rl)
c (j)
ɲ (ny)
ʎ (ly)
k (k)
ŋ (ng)
ɻ (r)
j (y)
w (w)
Table 2: Gurindji vowel inventory (Meakins et al., 2013). Orthography is in parentheses.
Front
High
Low
ɪ (i), ɪ: (ii)
Central
ɐ (a), ɐ: (aa)
Back
ʊ (u), ʊ: (uu)
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Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
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and palatal stops may possess corresponding glide allophones, and alveolar stops flapped
or tapped allophones. Fricative allophones are less common but have been reported in
similar environments (Fletcher & Butcher, 2015; Dixon, 2002). In terms of positional factors, word initial lenition is generally dispreferred, although some Australian languages
have lenited allophones in word-initial position (Blevins, 2001). Lenition has been correlated with stress in Murrinh Patha (Mansfield, 2015) and Yir Yoront (Alpher, 1988). Most
reports of allophony are impressionistic; however, Ingram et al. (2008) investigate spectrographic data to identify a range of connected speech processes involving reduction in
Warlpiri, a Ngumpin-Yapa language related to Gurindji. These include: Stop voicing, trilling, nasal weakening, vocalization, deletion, nasal-stop cluster reduction, and labialization. Other than Ingram et al. (2008), much of the instrumental phonetic work conducted
on Australian languages has focused either on place of articulation (Bundgaard-Nielsen
et al., 2012, 2015; Butcher, 1995; Tabain, 2009; Tabain & Butcher, 2015) or on those
few languages that contrast two series of stops (Butcher, 2004; B. Evans & Merlan, 2004;
McKay, 1980; Stoakes et al., 2007). Here we address the resulting gap in our understanding of Australian languages, with respect to manner of articulation.
1.3. Known potential factors in stop lenition
1.3.1. Duration
One of the most commonly cited factors affecting lenition is rate of speech and segmental
duration (Donegan & Stampe, 1979; Gurevich, 2008; Lindblom, 1983, 1990; Shockey &
Gibbon, 1993; Zwicky, 1972). Kirchner summarizes the relationship (2001, pp. 217–218):
“…fast speech, by definition, involves shortening of articulatory gestures. This
shortening can mean one of two things: either the articulator reaches the target
constriction faster, or the constriction itself is shorter.”
It is under these conditions that we also expect articulatory undershoot resulting in acoustic lenition. Soler and Romero (1999), for example, find duration and degree of constriction to be highly and positively correlated in Spanish spirantization phenomena. In
the Scouse variety of English, Marotta and Barth (2005) find fricative and approximant
allophones to be successively shorter than their stop counterparts. Furthermore, the relationship is understood to be gradient rather than categorical. In American English, stop
lenition is reported to be increasingly frequent and pronounced at successively quicker
speech rates, and in successively less formal registers (Warner & Tucker, 2011). Kirchner
(2001, p. 4) proposes an implicational hierarchy to this effect, claiming that “if a consonant lenites in some context, at a given rate or register of speech, it also lenites in that
context at all faster rates or more casual registers of speech.” Taken together, these studies
would suggest that, ceteris paribus, the shorter the duration afforded to a constriction, the
less likely full constriction will be achieved.
1.3.2. Place of articulation
Place of articulation of the target segment has also been suggested to affect lenition.
Foley (1977), for example, proposes a strength hierarchy of places of articulation ordered
by their likelihood of undergoing lenition: Velar > bilabial > alveolar. Evidence supporting this is generally constrained to studies of the Romance languages—for example
Florentine Italian (Dalcher, 2006) and Balearic Catalan (Wheeler, 2005, pp. 320–324).
Divergent patterns are reported in many of the worlds languages (see Kaplan, 2010 for
a summary). Explanations for differences in lenition rates based on place of articulation
have been couched in terms of physiological and aerodynamic factors (see Lavoie, 2001,
pp. 133–138 for velars; Hualde & Nadeu, 2011 for bilabials).
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 5 of 32
Within Australia, evidence for place of articulation effects is typically marshaled from
the extensive reconstruction of diachronic sound changes. One of the most striking sound
changes affecting a number of Australian languages is word initial weakening and the
loss of stops consonants—a process affecting bilabial, laminal, and velar obstruents but to
the exclusion of apicals (Blevins, 2001; Koch, 2004). An additional set of well established
historical changes concern languages that formerly possessed a two-way stop contrast. In
a subset of these cases we find that the obstruent system has reduced to a single stop series
for all places of articulation except for the apicals where a stop contrast is maintained (as
for example in some dialects of the Yolngu languages) (Wood, 1978). The accepted path
for this phonological re-organization is an intermediate stage of stop-glide lenition affecting the lenis peripheral and laminal stops (Dixon, 2002). Similarly, in the synchronic
domain, Mansfield’s (2015) sociophonetic study of lenition in Murrinh Patha notes that
peripheral stops are more prone to lenite to approximants than coronal stops. Finally,
cross-linguistic surveys of morphophonological alternations similarly demonstrate that
peripheral and pre-palatal obstruents undergo lenition more frequently than their apical
counterparts (Round, 2010).
Nevertheless, there is also synchronic and diachronic evidence for apical lenition. Taps
are found as allophones of apical stop phonemes in a number of languages (see Dixon,
2002) and have been implicated in an intermediate stage of stop allophony preceding
the emergence of three rhotic phoneme systems in the Karnic languages inter alia (Breen,
1997; Dixon, 2002). Despite alternations between stops and taps seemingly constituting
lenition (i.e., shorter and less complete constrictions), there are no studies closely examining the acoustic properties of taps in Australian languages. Outside of Australia it has
been noted that realizations of intervocalic voiced stops, typically transcribed as ‘taps,’
may include some formant structure— a feature more commonly associated with approximants (as reported in American English [Warner & Tucker, 2011]). Since taps have only
been impressionistically noted in Australian languages, it is possible that the degree of
apical lenition has been understated.
1.3.3. Flanking vowel quality
The present study focuses on the realization of phonemic stops in intervocalic position,
widely accepted as the segmental environment most favourable for consonantal lenition
(Kirchner, 2001; Lass, 1984, p. 182).1 There is, however, ongoing research into whether
the quality of the flanking vowels themselves has a significant impact on lenition outcomes.
Within effort-based models (e.g., Kirchner, 2001, 2004), vocalic openness (or height) is
argued to influence lenition rates due to the greater tongue displacement required to
make oral closure. Perceptual-based models (e.g., Kingston, 2008) instead contend that
consonantal lenition is not sensitive to vocalic openness. Within a perceptual approach,
speakers are understood to attend to disparities in intensity between an affected (lenited)
segment and its neighbors. In this view, lenition is motivated by a constraint against
abrupt interruptions to the intensity contour of a particular prosodic unit, such as those
created by a fully occluded stop. Since the intensity differences between consonants and
vowels are much larger than the intensity differences between individual vowel qualities,
it is argued that consonantal openness is a significant factor in motivating lenition but
vocalic openness is not.
Empirical evidence on this issue however is scarce and, as of yet, inconclusive. Competing
evidence is found in studies of Spanish lenition alone: Simonet et al. (2012) find less
constricted realizations of /d/ after lower vowels than after high vowels, while Colet
1
Often this environment is extended to include ‘inter-continuant’ environments incorporating all flanking
domains involving vowels, glides, or liquids (cf. Kirchner’s [2001] ‘quasi-intervocalic’).
Art. 20, page 6 of 32
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
et al. (1999) and Ortega-Llebaria (2004) find more constricted realizations of /g/ between
low vowels. Straightforward expectations arising from claims of articulatory effort are
further complicated by the possibility of the consonant in question shifting its place of
articulation to co-articulate with the flanking vowels—or vice versa (cf. Carrasco et al.,
2012, p. 169). Saltzman and Munhall (1989) find that in cases where there are competing
constraints on articulators between vowels and consonants (e.g., [g] in environments
/aga/ and /igi/), the location but not degree of constriction for the consonant will vary as
a function of the overlapping vowel. There are even fewer studies of Australian languages
that have investigated effects of flanking vowel quality on lenition outcomes. Mansfield
(2015) reports that following vowel quality was not statistically significant in his study
of /p/ and /k/ lenition in the Australian language Murrinh Patha once lexical item was
included as a random effect.
We therefore include preceding and following vocalic environments in the current study
to probe if there are any significant differences in lenition outcomes on the basis of articulatory effort. We group the vowels based on their proximity to the target consonant’s constriction location. This differs from studies that split vocalic environment into ‘open’ and
‘non-open’ vowels. Instead we anticipate some effort reduction and therefore less lenition
for /p/ and /k/ in the environment of /u/ since the former involves lip rounding and the
latter involves tongue backing, both of which are articulatory features shared with /u/. In
the case of /t/ and /ʈ/ we cautiously anticipate greater co-articulation (and less lenition)
in the environment of /i/ due to tongue tip raising, in contrast with /a/ and /u/.
1.3.4. Domain position effects
One final relevant factor affecting lenition outcomes is the position of the target segment
within relevant domains. Escure (1977, p. 58) proposes an implicational hierarchy of
positions in which lenition operates. She observes that initial lenition is generally less
frequent than non-initial lenition at the level of the syllable, word, and utterance. The
proposed hierarchy claims that if a language exhibits lenition domain-initially, it will
also exhibit lenition in all other non-initial environments. While Escure’s implicational
hierarchy has been shown to be violated by a number of languages (see Bauer, 2008),
its basic proposal of a dispreference for domain initial lenition has been widely borne
out by cross-linguistic surveys (cf. Ségéral & Scheer, 2008). One explanation advanced
for this is the importance of preserving phonological information in word onsets, which
have been shown to contain acoustic cues critical to word-perception (Marslen-Wilson &
Zwitserlood, 1989).
It is also the case that position affects duration (Oller, 1973; Edwards et al., 1991;
Tabain, 2003; Cho, 2006), which in turn affects lenition (Section 1.3.1). Consequently we
will be interested in this study to probe whether the contributions to lenition of duration
and position are to some degree independent.
Finally, usage-based models (e.g., Bybee, Pierrehumbert) predict that tokens in high
frequency lexical items are more prone to lenite than tokens in low frequency lexical
items.2 Such a prediction has been borne out by several lenition studies (Bybee, 2002;
Pierrehumbert, 2001; Dalcher, 2006) and so lexical item is included in the present study
as a random effect.
1.3.5. The abstract representation of segments
The concrete articulation of a phonetic segment can be regarded as an execution of a
more abstract motor plan and/or phonological representation. Theories like Articulatory
2
Where ‘high’ and ‘low’ frequency may be variously defined as some percentage of most utilized lexical items
in the corpus/corpora.
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 7 of 32
Phonology (Browman & Goldstein, 1989) propose that such plans contain articulatory
targets that may or may not be physically reached given other constraints such as segment
duration. Specifically, sequential gestural units can be subject to effects of ‘intergestural
sliding’ (Saltzman & Munhall, 1989). That is, when speech rate increases, articulatory
gestures tend to ‘slide into each other,’ increasing their temporal overlap, and resulting in
the truncation of one or both adjacent gestures. Such processes are typically assumed to
be governed by point-attractor dynamics: Articulatory trajectories for a given gestural unit
converge on a single state over time, i.e., a single specified target (Saltzman & Munhall,
1989). If this were the case, we would expect any failure to reach the specified target to be
the result of duress, such as applied by temporal reduction. On the other hand, if articulatory trajectories need not converge on a single, point-like gestural target but rather
a window-like range, there would be grounds for speakers freely producing a range of
articulatory velocities and constriction degrees, at least partially independent of temporal
reduction.
Parrell (2011) examines Spanish /b/, which like Gurindji stop phonemes, has many
unoccluded, sonorous phonetic realizations. Parrell argues that a single, fully occluded
articulatory target is sufficient to account for the variation in Spanish /b/, with other realizations the result of articulatory undershoot due to short duration. Parrell also observes
that if Spanish /b/ had only an unoccluded target, then one would not expect occluded
variants, even under conditions of long duration, yet long, occluded stops are precisely
what are found. Like Spanish /b/, the stop phonemes of Gurindji are sometimes fully
occluded, thus we have no reason to believe they are represented or planned solely with
unoccluded targets. However, will we ask the question, of whether a single, fully occluded
target is sufficient to account for the Gurindji data, or whether it is more consistent with
there being a range of targets (or the target itself being represented as a range rather than
a point), which span full occlusion through to more open articulations.
To be able to answer these kinds of questions acoustically, it is necessary for studies
to be able to quantify gradient acoustic variation (such as that involved in stop lenition)
and query the extent to which, and the circumstances in which, speakers may diverge
from a kinematic system that assumes a point-like articulatory target and set temporal
constraints.
1.4. The need for robust techniques of acoustic, casual speech analysis
We aim to infer properties of lenition from acoustic, casual speech data. Ideally, one might
study lenition using articulatory data collected under laboratory conditions, however in
practice there are good reasons also to pursue alternatives. For many lesser-studied languages, acoustic recordings of casual speech already exist whereas controlled articulatory
data is unlikely for logistical reasons to become available in the near future. For languages
no longer spoken, acoustic recordings may be all we can ever access. It is reasonable also
to expect that casual speech will contain informative variation that may not be apparent in controlled lab speech; as Ohala (1996, p. 206) observes, “[t]he more we look at
connected speech in detail, the larger the ‘zoo’ of strange and exotic phonetic animals
becomes.” To understand lenition synchronically and diachronically, we wish to be able
to study as much of the ‘zoo’ as possible.
1.4.1. Challenges of acoustic speech segmentation
Notwithstanding the advantages just mentioned of acoustic, casual speech data, its
analysis presents well-known challenges. The segmentation of continuous speech into
discrete acoustic or phonetic units is a somewhat artificial task (Turk & Sugahara, 2006).
Ladefoged (2003, p. 103) cautions that “many segments [simply] don’t have clear beginnings and ends” and Fry (1979, p.117) goes so far as to declare that “[from the acoustic
Art. 20, page 8 of 32
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Gurindji stops
point of view] there are only sounds which are more like, and sounds which are less like
the vowels of voiced speech.” Concretely, the segmentation of speech sounds presents
three challenges: (i) Discretization, (ii) commensurability, and (iii) reproducibility. By
‘discretization,’ we mean the challenge of delineating the edges, by whatever means, of
speech sounds. Many speech sounds, whether viewed acoustically or articulatorily, have
no point-like onset and offset events, and consequently various proxies are resorted to
(Fant, 1973; Lavoie, 2001). Table 3 presents criteria employed for segmenting regular
‘oral stops’ in some recent studies of stop lenition.
By ‘commensurability’ we refer to the challenge of comparing across different segment types. For example, if one uses ‘bursts’ to define the right edge of a true phonetic
stop, how should this be compared to the right edge of allophonic variants such as taps
(Connell, 1991), fricated stops (Dalcher, 2006), or simple approximants? In Gurindji,
this is a pertinent challenge, as a pilot study (Ennever, 2014a) indicates that fewer than
60% of intervocalic stop phonemes’ realizations are true stops, with the proportion dropping as low as 19% for /k/, depending on its position. By ‘reproducibility’ we refer to the
challenge of reproducing another study’s results. In practice, due to the challenges of discretization and commensurability, transcription teams may invest significant resources
in securing inter-coder reliability, yet in doing so, can converge upon criteria and conventions that differ form those devised in another lab. Moreover, standard instruments
have their limits. Consider the stops displayed in Figure 1. The first appears to have a
‘break’ in F2 and F3 (cf. the analysis criteria listed in Table 3) while the second does not,
Table 3: Reported criteria used in stop assessments.
Source
Criteria for assessing segment as a ‘stop’
Mansfield (2015)
Significant break in vowel formants, without turbulent noise,
and with some sign of a release burst in the onset of the following vowel.
Bouavichith & Davidson (2013)
A cessation of F2 and F3 during the consonant, giving rise to a
period of silence (with voicing).
Marotta & Barth (2005), Ashby &
VOT less than half the duration of the entire segment.
Przedlacka (2011)
Colantoni & Marinescu (2010)
Visual inspection of spectrogram.
Hualde et al. (2011)
Start marked at the end of periodic cycles of the vowel. End
marked just before the burst release.
Dalcher (2006)
Total silence in the case of voiceless stops, or simply vocal fold
vibration in the case of voiced stops, a visible burst, and VOT.
Figure 1: Stops which appear to differ in the presence of a ‘break’ in F2 and F3: a. is displayed
with a dynamic range of 30dB and b. is the same stop displayed with a dynamic range of 45 dB.
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 9 of 32
yet Figures 1a, b depict the same token, visualized with different settings of spectrogram parameters—specifically, dynamic ranges of 30dB and 45dB respectively. Because
spectrograms paint all intensities as white below some threshold, they can represent
regions to the human eye as being ‘empty’ and uniform when in reality they are not, thus
distorting the underlying data and inviting false comparison and analysis.
Consequently, a major contribution of this paper is methodological. In Section 3 we
introduce a new method for delineating stop-like and approximant-like segments in a
manner which addresses our three challenges. It uses the time-varying profile of intensity
in certain frequency bands as a basis for discretizing the speech signal in terms of commensurable events (namely, threshold points in intensity velocity functions) in a fashion
which is reproducible because it is automated, and deterministic given the acoustic data.
Having delineated stop phonemes in this manner, we then measure the change of intensity (Δi) inside the segment, the peak intensity velocity (Pi) and the segment’s duration
(Di), each as reproducible measures of lenition and related quantities.
In previous research, measures of change of intensity (Δi) during a consonant have been
employed as quantitative indexes of lenition in studies of Florentine Italian, Spanish, and
American English (Bouavichith & Davidson, 2013; Colantoni & Marinescu, 2010; Dalcher,
2006; Lavoie, 2001; Lewis, 2001). Kingston (2008) and Hualde et al. (2011) in particular
employ measures of peak intensity velocity (Pi) as a measure of lenition, on the grounds
that more lenis variants have less abrupt acoustic transitions, making it difficult to demarcate their edges and hence determine where to measure Δi from. Thus the current study
advances a line of research that infers information about lenition from careful measures
of acoustic intensity. The novelty of our contribution is to couple this approach with a
reproducible method for segment delineation, including of lenited variants and in a manner commensurable with the delineation of fully occluded stops, and to provide explicit
arguments supporting the theoretical and empirical validity of the approach.
2. Materials
2.1. Speaker and recordings
Acoustic data are from 15 audio recordings of 1 female L1 Gurindji speaker, Violet Wadrill
Nanaku. All sound files were recorded using a Roland Edirol R-09 in mono at a sample
rate of 44.1 KHz with 16 bit resolution. The recordings were made by the second author
between 2007–2014, when Wadrill was 66–73 years of age. The recordings consist of 14
narratives and 1 procedural-style narrative.3 The recordings were not made with acoustic analysis in mind and were recorded outside where there were some fluctuations in
ambient noise levels. Generally when taking acoustic measures of intensity, it is best
practice to ensure that all recording conditions are tightly controlled for, including keeping the distance between speaker and microphone constant by means of a head-mounted
microphone or similar apparatus. The present study acknowledges this shortcoming but
presents the following as reasons for data suitability. Firstly, the study only utilizes relative changes in intensity over very small time intervals (generally 0–100 ms) as measures
of lenition. Absolute measures of intensity (which would be heavily impacted by any
number of recording conditions) were avoided. Therefore, it was less important for the
global recording conditions to remain constant and instead the central requirement was
that non-vocalic sources of variation did not change significantly during the articulations
under examination. Secondly, a process of audio-visual token pre-screening (detailed in
Section 2.2) was employed to ensure token suitability.
3
These recordings form a part of the larger Gurindji corpus developed by the second author.
Art. 20, page 10 of 32
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
2.1.1. Gurindji’s apical contrast in our recordings
Breen (2007) emphasizes that in many Australian languages, phonemic contrasts between
alveolar and retroflex apicals can be elusive in the speech of some individuals. Though
Gurindji has been described as possessing contrastive alveolar and retroflex apicals, the
contrast in Wadrill’s speech is not robust, if it exists at all. Consequently, tokens of /t/
and /ʈ/ are pooled in our analysis, though we also report unpooled summary statistics in
Section 5.1 (note that in word initial position, the contrast between /t/ and /ʈ/ is neutralized for all speakers).
2.1.2. Qualitative features of stop lenition in Gurindji
Ennever (2014b) finds Gurindji to be typical of Pama-Nyungan languages in that it lacks
fricative realizations of phonemic stops (cf. Section 1.2.1). In a qualitative analysis, he
finds no evidence of frication in apical and bilabial stop articulations, and only 5 velar
tokens (where n = 208) were found to exhibit signs of weak frication.4 Instead, lenition was observed to operate along a continuum that included: Fully occluded stops
(Figure 2a), weak approximants (Figure 2b), more canonical approximants (Figure 2c)
or taps (Figure 2d) in the case of apicals. These can be compared with a rare, weakly
fricated /k/ type (Figure 2e).5
The present study focuses on quantitative measures of lenition types exemplified in the
continuum as represented by Figures (a–d).6
2.2. Initial sampling of segment tokens
Candidate phoneme tokens were identified from transcripts made by the second author,
which appeared in intervocalic word-initial and intervocalic word-medial environments, i.e., V#_V and V_V. Tokens underwent audiovisual inspection in Praat (Boersma
& Weenink, 2015) to ensure that they were not bounded by unexpected pauses or nonvocalic segments. Tokens that did occur in such environments, or that showed aberrant
intensity profiles due to aperiodic background noise (as recordings were made outdoors)
were excluded from the study. During this stage all suitable tokens were annotated at a
single time point within the constriction, on a point-tier in a Praat Text-Grid. It is from
this minimal markup that the automatic method described immediately below determines
the boundaries of the segment and from which our relevant measures are derived.
3. An automated method for segmentation and analysis of stop phonemes
In this section we introduce an automated method for the acoustic analysis of stop
phonemes, developed by the third author, which responds to the challenges of discretization, commensurability, and reproducibility identified in Section 1.4.1. We describe the
method’s premise (Section 3.1, Section 3.2) and the segmentation procedure (Section 3.3).
We then evaluate its success and its sensitivity to parameter settings (Sections 3.4–3.6);
and assess the intensity-based measures derived from the segmented data (Section 3.7).
Code and documentation for the method are available online.7
4
5
6
7
The pre-palatal obstruent is typically realized with substantial degrees of frication in Gurindji, but we
assume these reflect affricate articulatory targets, i.e., a stop + fricative sequence (cf. Section 1.1.1).
Note that the frequency viewing range is extended to 0–8000 Hz for this token.
A reviewer comments that a full-spectrum measure of intensity could conflate two distinct lenition outcomes (e.g., spirantization and stop-to-glide lenition). This is why our method focuses on specific frequency
bands (see our evaluation of parameters used by the algorithim in Sections 3.4–3.7. For this study, we select
bands that allow us to examine the approximant-like phones found in Gurindji, however the same algorithm
with different parameter settings, and careful interpretation of their results, could be used to examine frication noise for example. This functionality allows for nuanced analysis of multiple lenition pathways.
https://github.com/erichround/stop_lenition.
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 11 of 32
Figure 2: Spectrograms illustrating the range of stop realizations in Gurindji.
3.1. Background: Kinematic constraints on articulation
Our aim was to develop a method of interrogating acoustic data, which enables one to
make meaningful inferences about articulation. Consequently, we begin with an overview
of constraints on articulation. An understanding of these will help us to assess how successful the acoustic method is.
Studies of voluntary physiological movement in speech and other domains (Cooke,
1980; Munhall et al., 1985; Ostry et al., 1987) reveal tight constraints that operate on the
relationships between the amplitude of a movement (Am), its duration (Dm), and its peak
velocity (Pm), which closely approximate (1), where k is constant, at least under similar
speaking rates (Adams et al., 1993).
(1)
A m = k.D m .Pm
Equation (1) describes a three-cornered trade-off between Am, Dm, and Pm; for example,
one might attain the same spatial magnitude of movement (Am) while decreasing that
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Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
movement’s duration (Dm) but only by increasing peak velocity (Pm); or if peak velocity
is held constant, then a decrease in duration necessarily entails a decrease in movement
amplitude, and so forth. True physiological systems do not match (1) exactly, but in a study
of lingual and laryngeal gestures, Munhall et al. (1985) find that the basic relationship in
(1) accounts for between 74% and 89% of the variance in measures of Am, Dm, and Pm.
Our automated method makes reference to acoustic measures corresponding to Am,
Dm, and Pm. One way we would know that our method had failed to correspond well to
articulation is if those acoustic measures do not closely obey an acoustic counterpart to
equation (1). We apply that test in Section 3.7.
3.2. Premise of the acoustic method
The method works by delimiting segments based on acoustic data, and subsequently
measuring properties of them such as duration and change of intensity.
The segments we wish to delimit are intervocalic consonants that range phonetically
from true stops to more approximant-like segments (Section 2.1.2). In order to delimit
these varied phonetic types in a commensurable manner, we focus on their shared articulatory properties, namely an early phase in which oral aperture decreases, and a later
phase when it increases. Full closure may or may not be achieved in between. Crucially,
as the aperture narrows appreciably, it causes an attenuation of the intensity of the
speech signal, and thus during these focal phases, there is a broad relationship between
(i) constricting/opening articulation, (ii) decreasing/increasing aperture size in the oral
tract, and (iii) decreasing/increasing intensity. Consequently, to infer relative degree of
constriction we measure relative intensity over time, i(t). A greater total change in intensity, Δi, corresponds to narrower constriction, thus less lenition. Following practice in the
processing of articulatory data, we identify landmarks for the delimitation of segments
using a first derivative with respect to time, of a directly measured quantity; for our intensity function i(t) we refer to that derivative as ‘intensity velocity,’ v(t). This is described
further in Section 3.3.
There are some complications we expect to encounter. In particular, some phonetic
events affect intensity but are not correlated directly with oral aperture and oral constricting articulations. For segments with complete closure, passive devoicing and release
bursts ought to complicate the relationship between intensity and constriction degree.
Passive devoicing becomes increasingly likely as fully occluded segments become longer
(Ohala, 1983)8 and has been described as affecting coronal stops in Tiwi (Anderson
& Maddieson, 1994), an Australian language whose obstruent inventory is similar to
Gurindji’s. Since cessation of voicing would remove the source of sound energy, it would
affect our intensity measures i(t) and v(t) without there being any corresponding change
in the position and velocity of the superlaryngeal articulators. This may cause particularly long, fully occluded stops to have particularly large measures of Δi. Conversely,
bursts at the release of a full occlusion would add a noise source that affects i(t) and
v(t) in a manner which is separate from the effect of constriction degree. This effect may
cause i(t) and v(t) at the right edge of a stop consonant to leap more rapidly during the
8
A reviewer enquired about our use of the term ‘passive devoicing’ in the context of stops that do not
phonologically contrast voicing. We follow Anderson and Maddieson (1994) here, who use the term to
describe phonetic properties of voicing in stops in Tiwi, an Australian language with an obstruent inventory
similar to Gurindji, lacking a phonological voicing contrast. They note that intervocalic stop tokens—which
are ‘voiced’ allophones—exhibited devoicing after 45–50 ms of voiced constriction. We find these same characteristics in the Gurindji data (cf. Ennever, 2014b, p. 118 ff.) and note that /k/ is more prone to devoicing
than /p/ as would be expected. We tentatively conclude that—like Tiwi—there is no reason to assume that
Gurindji stops are produced with an accompanying glottal opening gesture. Rather the timing and nature of
a devoicing is consistent with Keating’s (1984) account of passive devoicing of voiced stops in English.
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 13 of 32
burst than would be expected on the basis of superlaryngeal articulatory movement. To
avoid this, our main measure of lenition will be derived from properties of the left edge
of consonants.
More generally, we did not expect the relationship between intensity and articulation to
hold equally well for all frequency bands in the spectrum. Higher frequencies associated
with frication noise would relate to constriction in a more complex manner than we have
just described. Low frequencies would also depart from the expectations described above,
since they travel more readily through the walls of the vocal tract, providing in effect an
acoustic side channel, whose intensity properties are not obviously linked to oral aperture
and articulator position. Consequently, in designing our method we explicitly tested the
utility of various frequency bands, described in Sections 3.4–3.6.
3.3. Automatic analysis and segmentation
Automatic processing was performed by custom scripts in R (R Core Team, 2016). Sound
files in .WAV format were bandpassed by calling the Filter (pass Hann band) function of
Praat (Boersma & Weenink, 2015) with a smoothing parameter of 50 Hz. Ultimately, we
identified the band 400–1200 Hz to be optimal for our purposes. However, we also tested
alternatives. These are assessed in Sections 3.4–3.7.
From each bandpassed sound file, a series of discrete intensity measures {i(t1), i(t2) … i(tn)}
was extracted, with intensity analysis window of 0.01s and time step of 0.0025s, using
Praat’s To Intensity function. To this we fit a continuous, cubic spline curve i(t) using
smooth.spline (R Core Team, 2016) with the smoothing parameter spar = 0.7. From the
continuous function i(t), we calculated a first derivative with respect to time: ‘intensity
velocity’ v(t). The value 0.7 of spar was chosen by experimentation, optimizing for the
plausibility of the curves generated for i(t) and v(t); alternative values are discussed in
Section 3.6.
Edges of segments were inferred from the function v(t). When articulatory closure commences, intensity i(t) begins to drop and intensity velocity v(t) shifts rapidly to some
maximum magnitude, max(|v(t)|). The demarcation algorithm uses this fact and proceeds
in two steps. In our Praat TextGrid (Section 2.2) we had annotated a point somewhere
within each stop, close to its beginning. The algorithm searches rightward from that
‘origin’ point and identifies an extremum in v(t). It then delimits the left edge of the
segment by selecting the moment, leading up to that extremum, when intensity velocity
v(t) hits a threshold level of 0.6*max(|v(t)|). This demarcation point defines the beginning, not of complete closure, but of the inferred closing gesture, as intensity falls. In its
second step, the algorithm searches rightwards again for the rise in i(t), and associated
v(t) extremum, corresponding to the opening gesture. Similarly, it demarcates the start
of the opening gesture using a threshold level of 0.6*max(|v(t)|). Our definition of segment edges in terms of thresholds in a velocity function follows standard practice in the
processing of articulatory data (cf. Kroos et al., 1997) obtained using techniques such as
EMA (Schönle et al., 1987). The 60% cut-off was determined by experimentation and is
evaluated in Section 3.5.
We emphasize that all segments’ edges are defined in terms of the start of closing and
opening gestures—properties which are shared by all of the phonetic segment types we
are interested in, whether fully occluded or highly lenited. Having delimited segments in
this commensurable way, we then extracted further commensurable metrics, such as its
duration Di; the magnitude of change of intensity Δi within the segment, defined as the
drop in intensity i(t) from the segment’s left edge to the lowest point it reaches; and peak
intensity velocity Pi, defined as the greatest absolute magnitude of v(t) during the segment’s phase of falling intensity.
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Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
3.4. Assessing the method
Our aim was an acoustic method that is informative about articulation, and in Section 3.2
we hypthothesized that some frequency bands should be more suited to this than others.
In the following sections we assess various frequency bands and values of spar, the cubic
spline smoothing parameter: We examine the algorithm’s success rate for delimiting segments in Section 3.5; the quality of its delimitations in Section 3.6; the sensitivity of the
derived measures Di, Δi, and Pi to parameter choices in Section 3.7; and algebraic properties of the i(t) curve in comparison to properties of articulatory movements in Section 3.8.
3.5. Success rates for segment delimitation
Our algorithm delimits segments by finding a fall–rise–fall contour in its intensity velocity
profile, v(t). Failures to delimit a segment can result from the absence of such a pattern
in a given frequency band, or from the smoothing procedure yielding a signal which is
either too noisy (insufficient smoothing) or too flat (excessive smoothing). We examined
success rates of segment delimitation across nine frequency bands and four values of spar.
We sort our nine frequency bands into four mnemonic classes: For frequencies which predominantly carry f0 energy, we examined two bands that we dub ‘voicing’ bands, 0–300 Hz,
0–400 Hz; for lower vocalic formants, we examined four ‘lower’ bands 300–1000 Hz,
400–1000 Hz, 400–1200 Hz, and 600–1400 Hz; for higher formants we examined two
‘upper’ bands, 1000–3200 Hz and 1200–3200 Hz; and for frication noise, one ‘noise’ band,
3200–10,000 Hz. Comparisons between band types, e.g., ‘voicing’ versus ‘lower’ should
reveal which broad spectral zones provide better performance. Comparisons within band
types, e.g., 300–1000 Hz versus 400–1000 Hz act as a sensitivity analysis, indicating the
extent to which precise choices of upper and lower frequencies may sway our results.
From the phonetic reasoning in Section 3.2 we predicted that segment delimitation using
the ‘voicing’ and ‘noise’ bands would be inferior to delimitation using the ‘lower’ and
‘upper’ bands.
We compared four settings of the smoothing parameter, spar = {0.5, 0.6, 0.7, 0.8}.
Given that we had already chosen spar = 0.7 on the basis that it produced the visually
most plausible i(t) and v(t) functions, our prediction was that a parameter setting of 0.7
would outperform the others when we assessed it quantitatively.
Comparisons of the success rates for segment delimitation according to band choice and
spar value are shown in Table 4. In this test, we ask only whether the algorithm was able
to find a fall–rise–fall pattern in v(t) and, on that basis, to delimit the segment. Additional
questions, such as the segmentation’s quality, are examined in Sections 3.6–3.8 below (in
Section 3.8 we will see why spar = 0.7 stands out against the other spar values).
Success rates for segment delimitation were high in general. Comparing frequency
band types, the algorithm succeeded as our phonetic reasoning predicted Rates were
Table 4: Success rates for segment delimitation (n = 586 segments).
Frequency band
‘Voicing’
‘Lower’
‘Upper’
‘Noise’
0–300 Hz
0–400 Hz
300–1000 Hz
400–1000 Hz
400–1200 Hz
600–1400 Hz
1000–3200 Hz
1200–3200 Hz
3200–10 000 Hz
spar = 0.5
spar = 0.6
0.94
0.96
0.99
0.99
0.99
0.98
0.96
0.96
0.85
0.93
0.96
0.99
0.99
0.99
0.99
0.97
0.98
0.89
spar = 0.7 spar = 0.8
0.92
0.94
0.99
0.99
0.99
0.99
0.97
0.97
0.86
0.85
0.88
0.97
0.97
0.97
0.97
0.94
0.90
0.76
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Art. 20, page 15 of 32
highest for the ‘lower’ and ‘upper’ bands, lower for the ‘voicing’ bands, and lower again
for the ‘noise’ band. Comparing within band types, the exact choice of frequency range
had little effect on success rates; this suggests that the procedure is robust and is not
dependent on highly specific settings of the frequency parameters. Comparing among
spar values, the ‘lower’ bands show little variation, other than slight decline in success
rates for spar = 0.8, due to excessive smoothing. For other band types, only spar = 0.8
with excessive smoothing shows any notable decline relative to the other values. This
likewise indicates that the procedure is robust and is not dependent on highly specific
parameter settings.
3.6. Segmentation quality
Once our algorithm finds the fall–rise–fall pattern it expects in v(t), it delimits segment
edges using a threshold multiple of the intensity velocity extremum. Experimentation with
thresholds between 0.2*max(|v(t)|) and 0.75*max(|v(t)|) showed that 0.6*max(|v(t)|)
yielded the best results. Figures 3a–d display the demarcations made for a number of
stop tokens with respect to their spectrograms. Smoothed intensity i(t) is represented by
the dotted curve, intensity velocity v(t) is represented by the solid curve, and the vertical
lines show the segments’ edges according to our method. Note that these demarcations do
not necessarily correspond to where a human annotator would place an annotation, since
whereas a human annotator will use any of a number of delimitation criteria depending
Figure 3: Example stop demarcations using spar = 0.7 and a delimitation threshold of 0.6*max(|v(t)|)
for tokens of /k/ (a, b), /p/ (c) and /t/ (d).
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Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
on the phonetic type of the token at hand, our algorithm applies the principle to all
tokens, to mark beginnings of articulatory closure and opening.
Thresholds lower than 0.6*max(|v(t)|) led to the left edge of segments being placed
inside a preceding vowel in cases where the vowel gradually tapered in its intensity over
time. Higher thresholds caused some bursts to be overlooked, leading to right edges being
placed too late. Using the 400–1200 Hz frequency band and spar = 0.7, the algorithm
using a 60% threshold delimited 581 of 586 stop phonemes. The edges it selected were
manually inspected, and none were judged to be problematic.
For those segments which had bursts (n = 112) we also compared the position of
the burst’s onset as judged by a human annotator, against the position inferred by the
algorithm, using frequency band 400–1200 Hz, spar = 0.7, and delimitation threshold
0.6*max(|v(t)|). As summarized in Table 5, both the mean and median differences were
small, on the order of 1% of the segment’s overall duration. This indicates that for datasets
of reasonable size, estimates of central tendency are of good quality. On the other hand,
the standard deviation as a proportion of segment duration was 0.1, indicating that the
inferred burst onset of individual tokens can differ from those judged by a human annotator. It is conceivable that the underlying cause of variation in our measurements might,
for some other datasets, lead to a bias in estimates of central tendency, and we suggest
that at least a subset of the inferred delimitations be compared with manual annotation,
as we have done here. In future research, a customized module for better handling bursts
would be a valuable addition to the method we present here.
3.7. Sensitivity of derived measures to parameter choices
In Section 3.5 we saw that exact settings of frequency bands had little effect on the algorithm’s rate of successful segment delimitation. To further evaluate our method’s sensitivity to small changes in band parameters, we compared inferred values from the ‘lower’
bands, 300–1000 Hz, 400–1000 Hz, 400–1200 Hz, and 600–1400 Hz for: Duration Di,
magnitude of change of intensity Δi, and peak intensity velocity Pi. Table 6 presents
Table 5: Differences in burst onset position (human – automated) (n = 112 segments).
Mean
Median
SD
Absolute
As proportion of
difference (s)
segment duration
0.00011
0.0011
0.0081
0.0015
0.014
0.10
Table 6: Measures inferred using ‘lower’ bands, compared across pairs of bands.
Difference of means (as proportion)
Correlation, r
300–1000 Hz 400–1000 Hz 400–1200 Hz 300–1000 Hz 400–1000 Hz 400–1200 Hz
Di (s)
400–1000 Hz
400–1200 Hz
600–1400 Hz
Δi (dB)
400–1000 Hz
400–1200 Hz
600–1400 Hz
Pi (dB/s) 400–1000 Hz
400–1200 Hz
600–1400 Hz
0.01
0.02
0.00
0.11
0.11
0.15
0.14
0.13
0.17
0.00
0.01
0.01
0.01
0.04
0.05
0.00
0.04
0.04
0.96
0.95
0.84
0.96
0.95
0.83
0.92
0.91
0.75
1.00
0.88
0.89
1.00
0.87
0.88
1.00
0.79
0.80
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
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Art. 20, page 17 of 32
pairwise comparison of values obtained for each of the ‘lower’ bands. Comparisons
shown are (i) the difference of means (expressed as a proportion of the larger of the
two), which indicates the magnitude of overarching disparity, or relative bias, between
bands; and (ii) linear correlation (Pearson’s r), which indicates the degree to which
the disparity between a pair of bands resembles a simple, linear shift, or departs
from that.
The expectation is that diagonals in Table 6, shown in italics, will show the lowest
levels of disparity, since these compare bands that overlap the most, and this expectation
is generally met. For duration D, differences of means are trivial, implying that there is
little bias towards longer or shorter estimates, as the precise boundaries of the frequency
bands are varied. Correlations are also high. For change of intensity, Δi, and peak intensity velocity, P, the expectation is that there will be some disparity among bands, since
intensity levels in different places in the spectrum are not expected to be the same. In view
of that, it is interesting that bands 400–1000 Hz and 400–1200 Hz are very similar. In
sum, we find that particularly in the range of 400–1100 ± 100 Hz, small changes to the
precise band settings have little impact on derived measures of D, Δi, and P: In this part of
the spectrum, our method is robust; its results are unlikely to be swayed by minor choices
among possible frequency parameters.
3.8. Evaluating the method’s premise: Algebraic properties of derived measures
The premise of our method is that since articulator height correlates with oral aperture
and thus with attenuation of intensity (in appropriate frequency ranges), it should
be possible to use i(t) as a broad proxy for articulator height and v(t) for articulator velocity. If this is correct, certain algebraic properties of articulator movements
(Section 3.1) should carry over to i(t) and to measures based on it, Di, and Pi. If such
properties did not carry over, then this must count as evidence against the validity of
our premise. Munhall et al. (1985) show duration Dm, amplitude Am, and peak velocity Pm of articulation relate approximately as in (1). The linear relationship between
Am and Dm.Pm arises when physically constrained motoric movements are optimized to
minimize sudden changes in acceleration, or ‘jerk’ (Flash & Hogan, 1985; Ostry et al.,
1987).
(1)
A m = k.D m .Pm
In contrast to the existence of kinematic constraints which cause articulators to obey
equation (1), we are aware of no obvious equivalents, independent of articulation, which
would cause acoustic measures inferred from intensity to obey equation (2), where in (2)
the values Di, Δi, and Pi are inferred from intensity.
(2)
Δi = ki .Di .Pi
However, if the premise of our method is sound, then we nevertheless expect equation (2)
to hold, at least in those parts of the spectrum where intensity closely tracks articulation. We examine how closely our inferred measures Di, Δi, and Pi conform to (2) in two
ways. First, in Section 3.8.1 we examine correlations between Δi and Di.Pi, as we vary
our frequency bands and smoothing parameter spar. The hope is that the same parameter settings found advantageous in Sections 3.5–3.7 above are also in close accordance
with equation (2). Second, in Section 3.8.2 we take our best-performing parameters from
Sections 3.5–3.7 and perform a full regression test to ask how closely our derived measures
conform to equation (2).
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3.8.1. Linear correlations
As our first test, we measure the linear correlation of Δi versus Di.Pi. High conformity
would support our premise; low conformity would contradict it. Table 7 shows the
linear correlation (Pearson’s r) of Δi and Di.Pi, for our nine frequency bands and four
values of the cubic spline smoothing parameter spar. Higher correlation values indicate
a closer conformity to (2), and thus by hypothesis, a closer nexus between intensity and
articulation.
We interpret these results as follows. Broadly speaking, the greater the degree of
smoothing applied to the underlying time series {i(t1), i(t2) … i(tn)}, the more the resulting continuous function i(t) and its derived measures Di, Δi, and Pi, come to conform
to equation (2). Interpreting this cautiously, this may arise because smoothing removes
noise which otherwise obscures genuine similarities between intensity and articulation,
but it may also be that smoothing reduces jerk and so coerces the data towards a function
i(t), whose derived measures Di, Δi, and Pi, happen to have the properties in (2), or there
may be an element of both. However, it can be observed that not all frequency bands
are alike. Both the ‘voicing’ and ‘noise’ bands conform less well to equation (2) than the
‘lower’ and ‘upper’ band. There is no reason from the mathematics of spline fitting why
this should be so, whereas the observation fits with our predictions, reasoned on phonetic
grounds, regarding which bands should more closely mirror articulation. This suggests to
us that spectral energy in ‘lower’ bands is a good choice of proxy for degree of constriction, and hence articulation.
3.8.2. Regression testing
In Section 3.8.1 we examined the relationships solely between Di, Δi, and Pi. Here we
apply a more exacting test, asking also how place of articulation, neighboring vowel,
position (word-internal versus -medial), and carrier word might affect that relationship.
We do this by means of a linear mixed-effects regression model, with carrier word as a
random effect. To be clear about what we are attempting to do here: The equation in (2)
has just two degrees of freedom, so that if one specifies Di, and Pi then Δi should be fully
predicted. Thus, if our acoustic measures conform to equation (2), we expect that in
our regression model Di, and Pi will overwhelmingly account for the variation in Δi. If
additional contributions come from the other factors, even if statistically significant, we
expect their effects to be small in magnitude. If that is the case, it offers more reason to
believe that our acoustic method is closely mirroring articulation.
Our regression model is summarized in Table 8; variables are explained below. Note
that in order to keep the key terms additive, we use the equivalent of the logarithm of
equation (2), ln(Δi) = kʹi + ln(Di) + ln(Pi), where kʹI becomes an intercept term.
Table 7: Correlation of D and A/P, by frequency band and spline smoothing parameter.
Linear correlation, r, of Δi and Di.Pi,
Frequency band
‘Voicing’
‘Lower’
‘Upper’
‘Noise’
0–300 Hz
0–400 Hz
300–1000 Hz
400–1000 Hz
400–1200 Hz
600–1400 Hz
1000–3200 Hz
1200–3200 Hz
3200–10 000 Hz
for spar = 0.5 spar = 0.6
0.75
0.68
0.69
0.62
0.64
0.64
0.60
0.51
0.48
0.78
0.79
0.66
0.66
0.59
0.55
0.60
0.55
0.65
spar = 0.7
spar = 0.8
0.56
0.79
0.91
0.91
0.93
0.90
0.57
0.62
0.61
0.87
0.57
0.97
0.96
0.96
0.95
0.94
0.69
0.79
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 19 of 32
The variable Phoneme has three levels. As noted in Section 2.1.1 /t/ and /ʈ/ are pooled
word medially as /T/; in initial position /ʈ/ does not occur. For vocalic environment, the
dataset was not sufficiently large to test each preceding vowel /i,a,u/ in combination with
each possible following vowel (3 × 3 = 9 conditions). Instead, for each stop phoneme we
binarily coded the vowel system into vowels that were/were not articulatorily proximal
with each stop phoneme as per Table 9 (see Section 1.3.3 for discussion). The resulting
binary true/false values for each stop-vowel combination are provided below.
Token counts for each stop phoneme, in environments neighboring true/false proximal
vowels to the left and the right, are shown in Table 10.
In total, 581 segments were delimited successfully (out of 586 which had been manually marked-up; see Section 2.2). Speaker was not added as a random effect because the
data comes from only 1 speaker. We used a simple additive model because there were not
enough data points to test interactions. The model was run using lmerTest (Kuznetsova,
2016) to test for significant predictors and MuMIn (Bartoń, 2016) to provide an R2C value
for the model.9 Results are presented in Table 11.
As predicted, the model explains close to 100% of the variation in Δi (R2C = 0.98). It
shows that the longer the duration of the stop, the greater the change of intensity Δi and
hence the less likely it is to be lenited (p < 0.001), and similarly, the higher the peak
velocity, the greater the change of intensity Δi and hence, the less likely a stop will be
lenited (p < 0.001). The model suggests some effects of phoneme type, i.e., /p/ does not
lenite to the same degree as /T/ (p < 0.05) and some effects for environment, i.e., a stop
Table 8: Variables potentially affecting the magnitude of Δi.
Dependant:
Fixed effects:
Random effect:
Log of change in intensity, ln(Δi)
Log of duration, ln(Di)
Log of peak velocity, P
Phoneme
Environment
continuous (log-dB)
continuous (log-s)
continuous (log-dB/s)
categorical {/k/, /p/, /T/}
categorical, {initial, medial}
Proximal Preceding V
categorical, {true, false}
Proximal Following V
categorical, {true, false}
Carrier Word
Table 9: Binary variables used for vocalic environment.
/p/
/k/
/T/
Prox = True
Prox = False
/u/
/u/
/i/
/a/, /i/
/a/, /i/
/a/, /u/
Table 10: Phoneme token counts by vocalic context.
/p/
/k/
/T/
9
Preceding
True__
False__
26
123
208
35
35
154
Following
__True
__False
30
119
231
12
55
134
Conditional R2 was used because it calculates variance based on both fixed and random effects and
therefore takes account of all factors which are contributing to variation in the data set (Nakagawa &
Schielzeth, 2013, p. 136). R2C was calculated using the MuMIn package in R (Bartoń, 2016).
Art. 20, page 20 of 32
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Table 11: Summary of linear mixed effects model. REML criterion at convergence: –1456.3.
a. Scaled residuals:
Min
–5.9523
1Q
–0.4464
Median
0.0689
3Q
0.4980
Max
3.5655
b. Random effects:
Groups
Carrier Word
Name
(Intercept)
Variance
0.0008455
SD
0.02908
Number of obs: 581, groups: Carrier Word, 334.
c. Fixed effects:
(Intercept)
Peak Velocity
Duration
Phoneme /p/
Phoneme /k/
Environment
Proximal preceding V
Proximal following V
Estimate
–7.335972
1.02745
0.92566
–0.017768
0.011209
0.016245
0.020114
–0.008889
SE
0.070012
0.008916
0.016038
0.008812
0.010830
0.007169
0.007902
0.008688
df
t value
545.7
572.9
517.6
190.3
220.3
170.8
409.5
202.2
–104.781
115.097
57.676
–2.016
1.035
2.266
2.545
–1.023
p value
<0.001
<0.001
<0.001
<0.05
0.3018
<0.05
<0.05
0.15305
is more likely to be lenited when it occurs word medially (p < 0.05) and after a proximal
vowel (p < 0.01). However, as predicted, the effect sizes of each of these contributions are
very small when compared to the contributions of peak velocity and duration. Although
they are statistically significant, they barely contribute to accounting for Δi.
In sum, the regression analysis confirms expectations about our acoustic measures. They
are behaving algebraically like the articulatory properties they are supposed to mimic. As
discussed earlier, there is no inherent reason for them to do that, unless they are tracking
articulation closely.
3.9. Summary and comparison with alternative ‘automated methods’
We have now introduced a quantitative method for measuring Di, Δi, and Pi from acoustic
data. The method applies commensurably to fully occluded and more lenited segments.
We have tested the method and ascertained that it is highly successful at delimiting segments, delimits them in a reasonable fashion, and is not overly sensitive to small differences in parameter values. It behaves as we expected based on phonetic reasoning, and
appears to mimic articulation well. Optimal settings are a frequency band of 400–1200 Hz
and a spar parameter of 0.7.
The evidence in Section 3.7, that our acoustic measure corresponds well with articulation, accords with an explicit comparison of acoustic and articulatory measures of lenition
in Spanish /b/ by Parrell (2010), which found them broadly comparable, although Parrell
only investigates equivalents of our Δi vis-vis Am; a measure of Pi is examined but is compared not with Pm but with Am. In combined acoustic–articulatory studies, we advocate
making the comparisons we have made here.
Our method differs from existing quantitative acoustic methods, employed by Hualde
et al. (2011), Carrasco et al. (2012) inter alia, in several respects. In Sections 3–3.7 we
(i) presented an explicit phonetic rationale for why we expect our procedure to work,
which relates acoustics to articulation and articulation to its kinematic constraints;
(ii) assessed multiple parameters and parameter settings, and related this back to the
phonetic rationale; and (iii) targeted spectral energy in frequency bands that we find most
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 21 of 32
closely mirror articulatory aperture. Our measures differ in that we focus on the changes
of intensity Δi measured from the left-edge of the target consonant, where the articulation of different phonetic segment types will be largely comparable, rather than the right
edge where the presence versus absence of release burst makes them less so. Our method
provides a measurement of segment duration which is deterministic, and free from the
variability that affects manual annotation even under the best conditions. Thus, although
our focus in this paper is on intensity and lenition, we emphasize that the provision of a
reproducible method for measuring duration is in itself an important methodological step
forward. On a practical note, our method requires only a single manual point annotation
in Praat, placed somewhere within the stop, allowing for rapid dataset mark-up requiring
minimal labor and expertise.
4. Factors affecting stop lenition in Gurindji
Above, we introduced and then assessed an automated method for measuring stop lenition
using acoustic data. In this section we apply the method, and enquire about the nature of
gestural targets in the stop phonemes of a speaker of Gurindji. Our original research questions for Gurindji were (Section 1):
1. What is the range of realizations (in terms of lenition) of the phonemic stops
in Gurindji, and their relative frequencies?
2. Are these influenced by a stop’s place of articulation, preceding and proceeding vowels, and/or word boundary adjacency, and if so, how?
3. Is there evidence to support an analysis of Gurindji stop phonemes having a
single, fully-occluded articulatory target, with more lenited variants the product of undershoot due to short duration; or conversely, is there evidence for
more open articulatory targets also?
We first answer question (1) with standard summary statistics (Section 5.1). Our approach
to answering (2) and (3) is as follows. In Section 3.8 we showed that if the aim is to predict the magnitude of change in intensity Δi, which is our measure of lenition, then it is
possible to account for nearly all variation given duration D and peak intensity velocity P.
So, very nearly, D and P predict Δi. But, can P itself be predicted from D? If so, then
essentially, lenition is predictable from duration alone, in accordance with the point-like
model of articulatory targets (Section 1.3.5) within a Task Dynamic approach. On the
other hand, if D only weakly predicts P, and therefore on its own only weakly predicts Δi,
then that would accord with a more window-like interpretation of articulatory targets.
Of course, there are also possible contributions from place of articulation, neighboring
vowels, and boundary adjacency. Accordingly, to answer question (2) we use a second
linear mixed effects model, again using the packages lmerTest (Kuznetsova, 2016) and
MuMIn (Bartoń, 2016) in the R package stats (R Core Team, 2016). The dependent variable this time is peak intensity velocity, P, and we examine its relationships to duration, D,
phoneme place of articulation, adjacency to vowels, and adjacency to boundaries. If that
model can predict P with great accuracy, it will in turn predict Δi with great accuracy,
and will support to point-like model (while also informing us of the relative contributions
of our predictor variables). Results are presented in Section 5.2; we discuss research question (3) in Section 6.2.
4.1. Predictions
Our regression model seeks to examine the contributions to the value of P, of place of
articulation, neighboring vowels, and boundary adjacency, each separate from the contribution due to duration. Any factor which increases P independently of duration would
Art. 20, page 22 of 32
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
contribute to an increase in Δi, and thus a decrease in lenition. Predictions for known
factors affecting lenition are: 1. Stop phoneme tokens in word initial position will exhibit
significantly less lenition (meaning we predict higher P for them) than those in word medial
position (Section 1.3.4); 2. vowel quality will affect stop lenition based on co-articulation
between preceding and following vowels (Section 1.3.3); and 3. /T/ may undergo less
lenition (thus contribute to higher values of P) than/p/ and /k/ (Section 1.3.2).
Our predictions regarding the contribution of D to P remain more open. In an idealized scenario, if articulatory targets in Gurindji were purely point-like and there were no
undershoot, then increases in duration would need to be perfectly offset by decreases in
peak velocity, in order that the same target be reached consistently; this would lead to a
negative relationship between D and P. In a more realistic point-like target scenario, with
undershoot, we expect the relationship to be weaker than in the idealized case, but to
remain negative. If targets are not point-like, then it is an open question what the operative relationship might be between D and P.
5. Results: Factors affecting stop lenition
5.1. Summary statistics
Summary statistics are in Table 12. Figure 4 plots the distributions of values for D, Δi,
and P by phoneme and environment. Comparison of phonemes in initial versus medial
position indicate that duration is longer (higher values of D) and lenition is less pronounced (higher values of Δi) in initial positions. Duration D and degree of lenition Δi
also varies across phonemes. As observed in Section 3.8.1, the linear correlation between
D and Δi is significant and positive (Pearson’s r(581) = 0.68, p = 0.000), i.e., the association between duration and lenition is significant and negative.10
5.2. Linear mixed effects analysis
We conducted a second linear mixed effects analysis to answer whether place of articulation, the surrounding vowels, and/or boundary adjacency have an effect on peak intensity
velocity P, when one simultaneously takes into account duration D. Carrier word is added
as a random effect. Speaker was not added as a random effect because the data comes
from only 1 speaker. We used a simple additive model because there were not enough
data points to test interactions. Results are presented in Table 13. The model explains a
good amount of variation in the data set (R2C = 0.31).11
Table 12: Summary statistics: Duration (D), change in intensity (Δi) & peak intensity velocity (P).
D (s)
Position
Phoneme
initial
k
p
T
k
p
T
ʈ
T
medial
N
172
62
52
74
87
138
84
54
mean
0.068
0.081
0.068
0.061
0.071
0.058
0.059
0.058
Δi (dB)
SD
0.015
0.019
0.011
0.011
0.012
0.010
0.010
0.011
mean
22.19
26.44
19.48
16.36
22.24
19.15
20.17
17.55
P (dB/s)
SD
8.44
8.26
4.70
7.30
6.57
6.76
7.09
6.25
mean
SD
555.4
594.6
512.7
436.2
557.5
551.3
574.8
514.8
160.8
150.8
120.1
151.8
135.8
152.3
150.0
155.9
Recall that our proxy for lenition is Δi and that greater values of Δi reflect less lenition (or more stop-like
articulations). Therefore, a positive correlation between duration and Δi implies a negative correlation
between duration and ‘degree of lenition.’
11
Recall that conditional R2 (rather than a marginal R2) calculates variance based on both fixed and random effects and therefore takes account of all factors which are contributing to variation in the data set
(Nakagawa & Schielzeth, 2013, p. 136). R2C was calculated using the MuMIn package in R (Bartoń, 2016).
10
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 23 of 32
Figure 4: Distributions of duration, change in intensity and peak intensity velocity by phoneme
and position. These plots combine a standard box plot, which concisely marks quantiles, with a
violin plot, whose width shows the density of tokens observed across the range.
Table 13: Summary of linear mixed effects model. REML criterion at convergence: 7367.2.
a. Scaled residuals:
Min
–2.4533
1Q
–0.6219
Median
0.0474
3Q
0.6679
Max
2.4635
b. Random effects:
Groups
Name
Carrier Word (Intercept)
Residual
Variance
5047
16901
SD
71.04
130.01
Number of obs: 581, groups: Carrier Word, 334.
c. Fixed effects:
(Intercept)
Duration
Phoneme /p/
Phoneme /k /
Environment
Proximal preceding V
Proximal following V
Estimate
392.41
2818.73
–15.14
–13.43
–29.55
–32.68
–1.06
SE
36.28
472.29
19.62
24.11
16.05
17.46
19.40
df
487.40
551.50
287.40
320.60
271.10
479.50
299.30
t value
p value
10.816
5.968
–0.772
–0.557
–1.841
–1.872
–0.055
<0.001
<0.001
0.4410
0.5780
0.0667
0.0618
0.9565
Interestingly, the model shows a relationship between D and P which is positive: The
longer the duration of the stop, the higher the peak velocity (p < 0.001). Neither proximal
vowels, word initial versus medial environment, nor the phoneme’s place of articulation
have a significant effect.
6. Discussion
6.1. Predictions versus findings
Our first prediction was that word medial position would promote greater lenition
than word initial position. This is true in absolute terms; however, the absolute effect
can be explained by duration (discussed below); once duration is controlled for, as in
Section 5.2, we find no contribution of word-medial position to enhanced lenition compared to word-initial position. A possible confounding factor, and one not explored here,
is stress (Section 1.1.2). A striking feature of many Australian languages is the cuing of
stress by the lengthening of post-tonic consonants (Fletcher & Butcher, 2015). In Warlpiri
(Ngumpin-Yapa), post-tonic consonants were found to be both lengthened and strengthened (Butcher & Harrington, 2003). Consequently, unlike in some other regions of the
globe, it may be unusual within the Australian context for word-initial (pre-tonic) consonants to show significantly longer (and hence less lenited) stop consonants than word
Art. 20, page 24 of 32
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
medially. Importantly though, we did not distinguish post-tonic consonants from other
medial consonants, nor do we have a satisfactory account of stress assignment. In any
case, it would appear the results differ from Warner and Tucker’s (2011) study of English
in which post-stress consonants were reduced in terms of duration but not in terms of
measures of lenition (intensity dip, cessation of formants, voicing). Further discussion
of the interplay between stress, consonant lengthening, and lenition effects awaits more
careful prosodic analyses of Gurindji.
Our second prediction was that flanking vowels that were articulatorily proximal
to the stop constriction would be less lenited than distal flanking vowels. The model
failed to confirm this. Our result instead mirrors the lack of significant effect of vocalic
environment on lenition found in Murrinh Patha (Mansfield, 2015), the only other
Australian language yet to be closely investigated for this effect. Taken together, these
studies raise the question of how many of the widely attested lenition patterns in
Australian languages are actually sensitive to vocalic environment. While preceding
vowels have been demonstrated to have effects on lenition outcomes in Spanish (cf.
Simonet et al., 2012; Ortega-Llebaria, 2004; Cole et al., 1999), perceptual-based
models of lenition (e.g., Kingston, 2008) still contend that lenition rates are unlikely
to be affected by vocalic openness. We encourage further research into the effects
of vocalic environment on lenition in Australian languages to help move this debate
forward.
Our third prediction was that /T/ would be less lenited than /p/ and /k/. This was not
confirmed by the model. We find no evidence in Gurindji to support any of the crosslinguistic place of articulation hierarchies (cf. Section 1.3.2) claimed to govern lenition
outcomes. When comparing this with our qualitative observations in Section 2.1.2 and
our discussion of articulatory tapping in Section 1.3.2, we suggest that a clear division
between peripheral and apical lenition is less likely to be borne out in Gurindji—and
other Australian languages—once the acoustic effects of tapping are investigated quantitatively as we have done here.
6.2. Evidence for articulatory targets
Finally we return to the question of articulatory targets for phonemic stops in Gurindji.
Here we emphasize that we take the results of 1 speaker only as suggestive for the possible
set of phonetic facts of the Gurindji phonological system and encourage further investigation into related languages where further data acquisition is possible. Our research
question was: Is there any evidence to support an analysis of Gurindji stop phonemes having a single, point-like, fully-occluded articulatory target, with more lenited variants the
product of undershoot due to short duration; or conversely, is there evidence for a range
of articulatory targets? If Gurindji stops have a single, fully-occluded articulatory target,
as proposed for Spanish voiced stops (Parrell, 2011), then this target ought to be reached,
given sufficient duration. If duration is too short, then articulation will undershoot the
target, to a degree which increases as duration continues to decrease. The result, at least
for undershot tokens, should be a tightly constrained, negative relationship between duration D and peak velocity P, and hence Δi. However, in Gurindji this is not the case.
Figure 5a plots D versus Δi. The key observation is that for any horizontal cut through
the data, corresponding to a single duration, one observes a wide range of Δi values, i.e.,
a wide variation in degree of lenition. This should only be possible if for a given duration,
the speaker is making use of a range of articulatory velocities (P), which is confirmed
visually in Figure 5b, and numerically in the R2C value of 0.31 for the regression model in
Section 5.2 (compare R2C = 0.98 in Section 3.8.2).
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
Art. 20, page 25 of 32
Figure 5: Change in intensity (Δi) versus (a) duration, (b) peak intensity velocity.
We can compare our results to studies that have considered the effects of ‘clock-rate’
on the realization of articulatory gestures. It is generally agreed that ‘clock slowing’12
will cause gestures to be longer, and to have less pronounced peak velocities for a given
displacement (Cho, 2001). However, if clock slowing is accompanied by larger target
displacements (e.g., fully occluded stops) then these articulations will typically involve
higher velocities (Byrd & Saltzman, 2003). The converse would be true if clock rate
increases were accompanied by resetting of target displacements: While there is a general
expectation for gestures to be shorter, or even truncated, under higher clock rates (Byrd
& Saltzman, 2003), if speakers are simultaneously setting various displacement targets,
then we will continue to observe a range of peak velocities. Such scenarios accord well
with our observations for Gurindji. For a phonemic stop token of a given duration, the
speaker in our study uses a range of velocities, some of which result in full occlusion
and others which do not. We regard this as incompatible with the assumption that the
speaker is aiming in all cases for a fully occluded target, since in many instances the
speaker evidently could articulate more rapidly and thereby reach the target, but does not
do so. One potential explanation of these observations comes from an alternative to the
traditional point-attractor model of articulatory phonology, in the form of target ‘ranges’
or ‘windows.’ Such a model is formalized by Keating (1990), who proposes that gestural
units are assigned individual target ‘windows’ which prescribe ranges of variability for
a given articulatory dimension. We can compare our findings with Warner and Tucker
(2011), who similarly argue that a ‘window’ model can account for variability in lenition phenomena in American English. They argue that conventionalized stop allophony
in American English defines a reasonably broad articulatory window in which stops may
be realized. Within the American English data however, the most significant constraint
governing the range of these target windows—more so than mechanical/durational factors—is the preservation of phonemic distinctions, specifically between the voiced and
voiceless stop series. In contrast, Gurindji, like many other Australia languages, lacks any
such voicing distinction. Since there is no need to preserve a phonemic laryngeal contrast
between two types of stop articulation, each stop phoneme is free to exploit a larger target
window. In this context, and in the light of our findings, an interesting priority for future
investigation is to improve our understanding of the extent to which articulatory targets
for obstruents of Australian languages can be understood as involving a ‘window’ of targets that vary in their constriction degree, while simultaneously preserving considerable
precision with respect to place of constriction.
12
Clock-slowing temporal modulation gestures are also called prosodic gesture [π-gestures] and are proposed
to have local affects in the region of phrasal boundaries.
Art. 20, page 26 of 32
Ennever et al: A replicable acoustic measure of lenition and the nature of variability in
Gurindji stops
7. Conclusions
We have measured lenition and related properties of phonemic stops in Gurindji from
acoustic fieldwork data using a novel method which is precise, minimizes data-markup
labor, and is automated and hence reproducible and scalable. We provided an extended
evaluation of the method and found convincing evidence that our measure of lenition
accords well with the properties of the articulatory system we are attempting to investigate. By attending to rates of change in intensity profiles v(t), the algorithm provides
deterministic, commensurate measures of segmental duration (Di) across different phonetic realizations of phonemic stops and generates a quantitative measure of lenition (Δi).
When applied to the Gurindji data, results revealed a language whose stop phonemes
span an extended space of lenition degrees, and whose patterns of lenition correspond, we
argue, not to a single articulatory target but to a range, or a ‘window’ of targets encompassing both fully and partially occluded postures. Contrary to expectations, beyond the
independent effect due to duration, we found no evidence of an extra positive effect on
lenition due to word-medial position. The fact that word initial stops were found to be
longer than their medial counterparts itself is of interest given that in Australian languages it is the post-tonic position that is typically lengthened and strengthened. Place of
articulation likewise showed no significant effect—a fact that we suggest has to do with
apicals freely leniting along a continuum towards taps, just as the peripheral stops lenite
along a continuum towards semi-vowels.
Acknowledgements
Research was supported by Australian Research Council (ARC) grant DE150101024 and
an ARC Centre of Excellence for the Dynamics of Language Small Grant to Erich Round.
The collection of the Gurindji data was funded by the Jaminjungan and Eastern Ngumpin
DoBeS project (CI Eva Schulze-Berndt) and Endangered Languages Documentation Project
(ELDP) grant IPF0134 to Felicity Meakins. We would like to thank Violett Wadrill for
the use of the Gurindji recordings. We would also like to thank our two anonymous
reviewers as well as Mike Proctor and audiences at the 2015 Australian Languages Society
Conference for constructive feedback and commentary. Any errors and omissions remain
our own.
Competing Interests
The authors have no competing interests to declare.
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How to cite this article: Ennever, T., Meakins, F. and Round, E. R. 2017 A replicable acoustic measure of lenition
and the nature of variability in Gurindji stops. Laboratory Phonology: Journal of the Association for Laboratory
Phonology 8(1): 20, pp. 1–32, DOI: https://doi.org/10.5334/labphon.18
Submitted: 15 April 2016
Accepted: 01 May 2017
Published: 22 August 2017
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