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Evaluating Automatic Speech Recognition for Child Speech Therapy Applications

Authors:

Adam Hair,

Kirrie J. Ballard,

Beena Ahmed,

Ricardo Gutierrez-OsunaAuthors Info & Claims

ASSETS '19: Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility

Pages 578 - 580

https://doi.org/10.1145/3308561.3354606

Published: 24 October 2019 Publication History

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Abstract

Automatic speech recognition (ASR) technology can be a useful tool in mobile apps for child speech therapy, empowering children to complete their practice with limited caregiver supervision. However, little is known about the feasibility of performing ASR on mobile devices, particularly when training data is limited. In this study, we investigated the performance of two low-resource ASR systems on disordered speech from children. We compared the open-source PocketSphinx (PS) recognizer using adapted acoustic models and a custom template-matching (TM) recognizer. TM and the adapted models significantly out-perform the default PS model. On average, maximum likelihood linear regression and maximum a posteriori adaptation increased PS accuracy from 59.4% to 63.8% and 80.0%, respectively, suggesting that the models successfully captured speaker-specific word production variations. TM reached a mean accuracy of 75.8%

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Cited By

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Aytutuldu IGenc YAkgul Y(2024)Audio-Only Phonetic Segment Classification Using Embeddings Learned From Audio and Ultrasound Tongue Imaging DataIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2024.347331632(4501-4510)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TASLP.2024.3473316
Fuad AAhmed SAnannya NMridha MNur K(2024)An Open-Source Voice Command-Based Human-Computer Interaction System Using Speech Recognition PlatformsProceedings of the 2nd International Conference on Big Data, IoT and Machine Learning10.1007/978-981-99-8937-9_36(527-545)Online publication date: 30-Mar-2024
https://doi.org/10.1007/978-981-99-8937-9_36
Sharma MPant LTewari RShelke CGayathri KSahu D(2024)Automatic Speech Recognition for Bilingual Children in Identification of Language DisorderInternational Conference on Signal, Machines, Automation, and Algorithm10.1007/978-981-97-6352-8_45(641-651)Online publication date: 19-Dec-2024
https://doi.org/10.1007/978-981-97-6352-8_45
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Index Terms

Evaluating Automatic Speech Recognition for Child Speech Therapy Applications
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Speech recognition

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Published In

ASSETS '19: Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility

October 2019

730 pages

ISBN:9781450366762

DOI:10.1145/3308561

General Chair:
Jeffrey P. Bigham
Carnegie Mellon University., USA
,
Program Chairs:
Shiri Azenkot
Cornell Tech, USA
,
Shaun K. Kane
University of Colorado Boulder, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2019

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Poster

Funding Sources

Qatar National Research Fund

Conference

ASSETS '19

Sponsor:

SIGACCESS

ASSETS '19: The 21st International ACM SIGACCESS Conference on Computers and Accessibility

October 28 - 30, 2019

PA, Pittsburgh, USA

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ASSETS '19 Paper Acceptance Rate 41 of 158 submissions, 26%;

Overall Acceptance Rate 436 of 1,556 submissions, 28%

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Cited By

View all

Aytutuldu IGenc YAkgul Y(2024)Audio-Only Phonetic Segment Classification Using Embeddings Learned From Audio and Ultrasound Tongue Imaging DataIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2024.347331632(4501-4510)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TASLP.2024.3473316
Fuad AAhmed SAnannya NMridha MNur K(2024)An Open-Source Voice Command-Based Human-Computer Interaction System Using Speech Recognition PlatformsProceedings of the 2nd International Conference on Big Data, IoT and Machine Learning10.1007/978-981-99-8937-9_36(527-545)Online publication date: 30-Mar-2024
https://doi.org/10.1007/978-981-99-8937-9_36
Sharma MPant LTewari RShelke CGayathri KSahu D(2024)Automatic Speech Recognition for Bilingual Children in Identification of Language DisorderInternational Conference on Signal, Machines, Automation, and Algorithm10.1007/978-981-97-6352-8_45(641-651)Online publication date: 19-Dec-2024
https://doi.org/10.1007/978-981-97-6352-8_45
Nyenooke EOmoruyi O(2023)Development Of An Interactive Language Learning Application For Children2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS)10.1109/ICMEAS58693.2023.10379361(1-5)Online publication date: 1-Nov-2023
https://doi.org/10.1109/ICMEAS58693.2023.10379361
McLeod SKelly GAhmed BBallard K(2023)Equitable access to speech practice for rural Australian children using the SayBananas! mobile gameInternational Journal of Speech-Language Pathology10.1080/17549507.2023.220505725:3(388-402)Online publication date: 25-May-2023
https://doi.org/10.1080/17549507.2023.2205057
Albudoor NPeña E(2022)Identifying Language Disorder in Bilingual Children Using Automatic Speech RecognitionJournal of Speech, Language, and Hearing Research10.1044/2022_JSLHR-21-0066765:7(2648-2661)Online publication date: 18-Jul-2022
https://doi.org/10.1044/2022_JSLHR-21-00667
McLeod SBallard KAhmed BMcGill NBrown M(2020)Supporting Children With Speech Sound Disorders During COVID-19 Restrictions: Technological SolutionsPerspectives of the ASHA Special Interest Groups10.1044/2020_PERSP-20-001285:6(1805-1808)Online publication date: 17-Dec-2020
https://doi.org/10.1044/2020_PERSP-20-00128

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