Modelling errors in automatic speech recognition for dysarthric speakers

Caballero Morales, Santiago-Omar and Cox, Stephen (2009) Modelling errors in automatic speech recognition for dysarthric speakers. EURASIP Journal on Advances in Signal Processing, 2009. ISSN 1687-6172

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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.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies
Depositing User:	Vishal Gautam
Date Deposited:	11 Mar 2011 17:02
Last Modified:	22 Apr 2023 00:39
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22371
DOI:	10.1155/2009/308340

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