Automatic speech recognition and training for severely dysarthric users of assistive technology: The STARDUST project

Parker, Marcie; Cunningham, Stuart; Enderby, Pam; Hawley, Mark; Green, Phil

doi:10.1080/02699200400026884

Cited by 42 publications

(28 citation statements)

References 9 publications

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“…For individuals with severe dysarthria, the vocabulary size of ASR systems tends to be extremely restricted. The STAR-DUST project [5], for instance, developed speech-controlled interfaces with a limited vocabulary of 10 isolated command words. In this paper, we aim to build a relatively large vocabulary ASR system, consisting of over 1500 words for speakers with severe to moderate dysarthria.…”

Section: Introductionmentioning

confidence: 99%

Adapting acoustic and lexical models to dysarthric speech

Mengistu

Rudzicz

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Dysarthria is a motor speech disorder resulting from neurological damage to the part of the brain that controls the physical production of speech and 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 WER by an average of 8.29% relative on a large vocabulary task of over 1500 words for 6 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 crossvalidation.

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Section: Introductionmentioning

confidence: 99%

Adapting acoustic and lexical models to dysarthric speech

Mengistu

Rudzicz

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The STARDUST Project (Speech Training And Recognition for Dysarthric Users of Speech Technology) [16,[27][28][29] has developed speech technology for people with severe dysarthria. Among the applications developed, an ECS (Environmental Control System) was designed for home control with a small vocabulary speaker-dependent recognizer (10 words commands).…”

Section: Introductionmentioning

confidence: 99%

Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers

Morales

Cox

2009

EURASIP J. Adv. Signal Process.

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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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“…We have previously developed techniques for using automatic speech recognition to provide feedback to patients practising their speech [25,27]. These approaches are based on using specially developed speech recognition software able to provide objective feedback, which acts as a substitute for the judgement of an expert listener, such as the speech and language therapist.…”

Section: Speech Therapymentioning

confidence: 99%

Remote Speech Technology for Speech Professionals - the CloudCAST initiative

Green

Marxer

Cunningham

et al. 2015

Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

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Clinical applications of speech technology face two challenges. The first is data sparsity. There is little data available to underpin techniques which are based on machine learning and, because it is difficult to collect disordered speech corpora, the only way to address this problem is by pooling what is produced from systems which are already in use. The second is personalisation. This field demands individual solutions, technology which adapts to its user rather than demanding that the user adapt to it. Here we introduce a project, CloudCAST, which addresses these two problems by making remote, adaptive technology available to professionals who work with speech: therapists, educators and clinicians.

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Automatic speech recognition and training for severely dysarthric users of assistive technology: The STARDUST project

Cited by 42 publications

References 9 publications

Adapting acoustic and lexical models to dysarthric speech

Adapting acoustic and lexical models to dysarthric speech

Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers

Remote Speech Technology for Speech Professionals - the CloudCAST initiative

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