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Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers

Abstract

Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of the muscles responsible for speech. Although automatic speech recognition (ASR) systems have been developed for disordered speech, factors such as low intelligibility and limited phonemic repertoire decrease speech recognition accuracy, making conventional speaker adaptation algorithms perform poorly on dysarthric speakers. In this work, rather than adapting the acoustic models, we model the errors made by the speaker and attempt to correct them. For this task, two techniques have been developed: (1) a set of "metamodels" that incorporate a model of the speaker's phonetic confusion matrix into the ASR process; (2) a cascade of weighted finite-state transducers at the confusion matrix, word, and language levels. Both techniques attempt to correct the errors made at the phonetic level and make use of a language model to find the best estimate of the correct word sequence. Our experiments show that both techniques outperform standard adaptation techniques.

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Correspondence to Santiago Omar Caballero Morales.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Caballero Morales, S.O., Cox, S.J. Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers. EURASIP J. Adv. Signal Process. 2009, 308340 (2009). https://doi.org/10.1155/2009/308340

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Keywords

  • Recognition Accuracy
  • Confusion Matrix
  • Automatic Speech Recognition
  • Acoustic Model
  • Speech Disorder