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The effect of task conditions on the comprehensibility of synthetic speech

Authors:

Jennifer Lai,

David Wood,

Michael ConsidineAuthors Info & Claims

CHI '00: Proceedings of the SIGCHI conference on Human Factors in Computing Systems

Pages 321 - 328

https://doi.org/10.1145/332040.332451

Published: 01 April 2000 Publication History

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Abstract

A study was conducted with 78 subjects to evaluate the comprehensibility of synthetic speech for various tasks ranging from short, simple e-mail messages to longer news articles on mostly obscure topics. Comprehension accuracy for each subject was measured for synthetic speech and for recorded human speech. Half the subjects were allowed to take notes while listening, the other half were not. Findings show that there was no significant difference in comprehension of synthetic speech among the five different text-to-speech engines used. Those subjects that did not take notes performed significantly worse for all synthetic voice tasks when compared to recorded speech tasks. Performance for synthetic speech in the non note-taking condition degraded as the task got longer and more complex. When taking notes, subjects also did significantly worse within the synthetic voice condition averaged across all six tasks. However, average performance scores for the last three tasks in this condition show comparable results for human and synthetic speech, reflective of a training effect.

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Cited By

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Abbott JKaye JClawson J(2022)Identifying an Aurally Distinct Phrase Set for Text Entry TechniquesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501897(1-13)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501897
Rodero ELucas I(2021)Synthetic versus human voices in audiobooks: The human emotional intimacy effectNew Media & Society10.1177/1461444821102414225:7(1746-1764)Online publication date: 28-Jun-2021
https://doi.org/10.1177/14614448211024142
Branaghan RO’Brian JHildebrand EFoster LBranaghan RO’Brian JHildebrand EFoster L(2021)DisplaysHumanizing Healthcare – Human Factors for Medical Device Design10.1007/978-3-030-64433-8_11(271-306)Online publication date: 22-Feb-2021
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Index Terms

The effect of task conditions on the comprehensibility of synthetic speech
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Speech recognition
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

CHI '00: Proceedings of the SIGCHI conference on Human Factors in Computing Systems

April 2000

587 pages

ISBN:1581132166

DOI:10.1145/332040

Chairmen:
Thea Turner
Motorola Labs
,
Gerd Szwillus
Univ. of Paderborn, Paderborn, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 April 2000

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CHI00

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CHI00: Human Factors in Computing Systems

April 1 - 6, 2000

The Hague, The Netherlands

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CHI '00 Paper Acceptance Rate 72 of 336 submissions, 21%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

View all

Abbott JKaye JClawson J(2022)Identifying an Aurally Distinct Phrase Set for Text Entry TechniquesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501897(1-13)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501897
Rodero ELucas I(2021)Synthetic versus human voices in audiobooks: The human emotional intimacy effectNew Media & Society10.1177/1461444821102414225:7(1746-1764)Online publication date: 28-Jun-2021
https://doi.org/10.1177/14614448211024142
Branaghan RO’Brian JHildebrand EFoster LBranaghan RO’Brian JHildebrand EFoster L(2021)DisplaysHumanizing Healthcare – Human Factors for Medical Device Design10.1007/978-3-030-64433-8_11(271-306)Online publication date: 22-Feb-2021
https://doi.org/10.1007/978-3-030-64433-8_11
Rodero E(2017)Effectiveness, attention, and recall of human and artificial voices in an advertising story. Prosody influence and functions of voicesComputers in Human Behavior10.1016/j.chb.2017.08.04477:C(336-346)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.chb.2017.08.044
Chang YLiao YTsai WYu L(2011)Evaluation of TTS systems in intelligibility and comprehension tasksProceedings of the 23rd Conference on Computational Linguistics and Speech Processing10.5555/2207055.2207060(64-78)Online publication date: 8-Sep-2011
https://dl.acm.org/doi/10.5555/2207055.2207060
Huang K(2006)Effect of Learning English as a Second Language on the Comprehension of Synthetic Speech in Ambient NoiseProceedings of the Human Factors and Ergonomics Society Annual Meeting10.1177/15419312060500177950:17(2084-2088)Online publication date: 1-Oct-2006
https://doi.org/10.1177/154193120605001779
Vadas KPatel NLyons KStarner TJacko JNieminen MRöykkee M(2006)Reading on-the-goProceedings of the 8th conference on Human-computer interaction with mobile devices and services10.1145/1152215.1152262(219-226)Online publication date: 12-Sep-2006
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Wattenberg T(2006)Accessibility heuristics utilizing learnability characteristics of synthesized speech applicationsACM SIGACCESS Accessibility and Computing10.1145/1127564.1127574(45-47)Online publication date: 1-Jan-2006
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Shajahan PIrani P(2006)One family, many voices: Can multiple synthetic voices be used as navigational cues in hierarchical interfaces?International Journal of Speech Technology10.1007/s10772-006-9000-79:1-2(1-15)Online publication date: 12-Oct-2006
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Catrambone RStasko JXiao J(2004)ECA as user interface paradigmFrom brows to trust10.5555/1138317.1138327(239-267)Online publication date: 1-Jan-2004
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