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Stop consonant voicing classification for computer-assisted speech training of patients with cleft lips and palates

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i-CREATe '07: Proceedings of the 1st international convention on Rehabilitation engineering & assistive technology: in conjunction with 1st Tan Tock Seng Hospital Neurorehabilitation Meeting

Pages 142 - 147

https://doi.org/10.1145/1328491.1328529

Published: 23 April 2007 Publication History

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Abstract

Cleft lips and palates (CLP) may cause functional disorders even after adequate surgical treatments, speech disorders being one of them. Automatic algorithms utilizing acoustic-phonetic knowledge are needed in developing computer-based tools for assisting the speech training of CLP patients. This work focuses on acoustic discrimination among voiced, voiceless unaspirated, and voiceless aspirated stop consonants with the same place of articulation and aims at revealing a set of acoustic measurements capable of discriminating a CLP patients' speech. Acoustic measurements based on duration and signal energy are proposed and studied. Analysis of variance and classification experiments demonstrate high potentials in using these acoustic measurements in developing automatic voicing classification algorithms for speech training tools. The overall classification accuracy of 92% is achieved in classifying non-CLP data, in which the best result obtained is 99% for the alveolar case. The proposed measurements can classify data from CLP patients with almost 90% accuracy even when the classifier is trained only on the non-CLP data.

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Cited By

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Likitsupin KSuchato APunyabukkana PWutiwiwatchai C(2008)Improving Segment-based Speech Recognition by Recovering Missing Segments in Segment Graphs ¿ A Thai Case Study2008 International Symposium on Communications and Information Technologies10.1109/ISCIT.2008.4700196(268-273)Online publication date: Oct-2008
https://doi.org/10.1109/ISCIT.2008.4700196

Index Terms

Stop consonant voicing classification for computer-assisted speech training of patients with cleft lips and palates
1. Computing methodologies
  1. Machine learning
2. Social and professional topics
  1. Professional topics
    1. Computing profession
      1. Assistive technologies
  2. User characteristics
    1. People with disabilities

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Published In

i-CREATe '07: Proceedings of the 1st international convention on Rehabilitation engineering & assistive technology: in conjunction with 1st Tan Tock Seng Hospital Neurorehabilitation Meeting

April 2007

272 pages

ISBN:9781595938527

DOI:10.1145/1328491

General Chair:
Zen T. H. KOH
START Centre (Singapore)

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 April 2007

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i-CREATe07

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SIGACCESS

i-CREATe07: International Conference for Rehabilitation Engineering & Assistive Technology

April 23 - 26, 2007

Singapore

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Cited By

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Likitsupin KSuchato APunyabukkana PWutiwiwatchai C(2008)Improving Segment-based Speech Recognition by Recovering Missing Segments in Segment Graphs ¿ A Thai Case Study2008 International Symposium on Communications and Information Technologies10.1109/ISCIT.2008.4700196(268-273)Online publication date: Oct-2008
https://doi.org/10.1109/ISCIT.2008.4700196

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Recommendations

Automatic detection of consonant omission in cleft palate speech

Voicing Silent Speech

Recognition of stop consonants by computer analysis (acoustics, phonetics, transforms, speech)