Adapting acoustic and lexical models to dysarthric speech
Abstract
Dysarthria is a motor speech disorder resulting from neurological damage to the part of the brain that controls the physical production of speech. It is, in part, characterized by pronunciation errors that include deletions, substitutions, insertions, and distortions of phonemes. These errors follow consistent intra-speaker patterns that we exploit through acoustic and lexical model adaptation to improve automatic speech recognition (ASR) on dysarthric speech. We show that acoustic model adaptation yields an average relative word error rate (WER) reduction of 36.99% and that pronunciation lexicon adaptation (PLA) further reduces the relative WER by an average of 8.29% on a large vocabulary task of over 1500 words for six speakers with severe to moderate dysarthria. PLA also shows an average relative WER reduction of 7.11% on speaker-dependent models evaluated using 5-fold cross-validation.
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No prior research published to date has used auxiliary features based speaker adaptation targeting dysarthric and elderly speech.
“In contrast, there were no prior researches published to date on auxiliary features based speaker adaptation targeting such data.”
From Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition (2022) · cites this paper as [77]
Existing speaker adaptation methods for dysarthric and elderly speech use mainly model based approaches.
“Existing speaker adaptation methods for dysarthric and elderly speech use mainly model based approaches [76], [77], [9], [78], [79], [11], [12], [36], [40], [37].”
From Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition (2022) · cites this paper as [77]
Only limited research on speaker adaptation techniques targeting dysarthric and elderly speech recognition has been conducted so far.
“In contrast, only limited research on speaker adaptation techniques targeting dysarthric and elderly speech recognition has been conducted so far.”
From Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition (2022) · cites this paper as [77]
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- Authors
- Mengistu, K. T., & Rudzicz, F.
- Affiliations
- University of Toronto (CA)
- ORCID
- Rudzicz, Frank
- Venue
- 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2011-05
- Pages
- 4924-4927
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2109848220
- Cited by
- 82
- References
- 11
- Citations by year
- 2025: 4 · 2024: 3 · 2023: 8 · 2022: 4 · 2021: 10 · 2020: 7 · 2019: 10 · 2018: 4 · 2017: 5 · 2016: 7 · 2015: 8 · 2014: 3 · 2013: 6 · 2012: 2
- FWCI
- 3.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- dysarthria, speech recognition, acoustic model adaptation, motor speech disorders, word error rate reduction, speaker-dependent models, large vocabulary speech recognition
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