Computer recognition of the speech of adults with cerebral palsy and dysarthria

Coleman, Colette L.; Meyers, Lawrence S.

doi:10.1080/07434619112331275663

Cited by 33 publications

(18 citation statements)

References 6 publications

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“…Usually, these involved small-vocabulary recognition tasks with word-recognition accuracies significantly lower for speakers with dysarthria, often at least 26.2% lower than the general population [27]. For example, given a vocabulary of 40 words, Noyes and Frankish [28] report mean word-recognition accuracies of 58.6% for speakers with dysarthria compared with 95% for the general population.…”

Section: A Speech Recognition For Speakers With Dysarthriamentioning

confidence: 99%

Articulatory Knowledge in the Recognition of Dysarthric Speech

Rudzicz

2011

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Disabled speech is not compatible with modern generative and acoustic-only models of speech recognition (ASR). This work considers the use of theoretical and empirical knowledge of the vocal tract for atypical speech in labeling segmented and unsegmented sequences. These combined models are compared against discriminative models such as neural networks, support vector machines, and conditional random fields. Results show significant improvements in accuracy over the baseline through the use of production knowledge. Furthermore, although the statistics of vocal tract movement do not appear to be transferable between regular and disabled speakers, transforming the space of the former given knowledge of the latter before retraining gives high accuracy. This work may be applied within components of assistive software for speakers with dysarthria.

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Section: A Speech Recognition For Speakers With Dysarthriamentioning

confidence: 99%

Articulatory Knowledge in the Recognition of Dysarthric Speech

Rudzicz

2011

IEEE Trans. Audio Speech Lang. Process.

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“…Dysarthric speech recognition has been investigated for more than two decades [10,11]. The efficacy of commercial systems has been limited for speakers with mild or mild-moderate dysarthria [12,13].…”

Section: Automatic Speech Recognition For Dysarthric Speechmentioning

confidence: 99%

Model adaptation and adaptive training for the recognition of dysarthric speech

Sehgal¹,

Cunningham²

2015

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

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Dysarthria is a neurological speech disorder, which exhibits multi-fold disturbances in the speech production system of an individual and can have a detrimental effect on the speech output. In addition to the data sparseness problems, dysarthric speech is characterised by inconsistencies in the acoustic space making it extremely challenging to model. This paper investigates a variety of baseline speaker independent (SI) systems and its suitability for adaptation. The study also explores the usefulness of speaker adaptive training (SAT) for implicitly annihilating inter-speaker variations in a dysarthric corpus. The paper implements a hybrid MLLR-MAP based approach to adapt the SI and SAT systems. ALL the results reported uses UA-SPEECH dysarthric data. Our best adapted systems gave a significant absolute gain of 11.05% (20.42% relative) over the last published best result in the literature. A statistical analysis performed across various systems and its specific implementation in modelling different dysarthric severity sub-groups, showed that, SAT-adapted systems were more applicable to handle disfluencies of more severe speech and SI systems prepared from typical speech were more apt for modelling speech with low level of severity.

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“…For instance, in [1] it was reported that for 10 adults with dysarthria and 13 control subjects, the dysarthric subjects had significantly fewer stimuli recognized by the computer than the Thanks to the Natural Sciences and Engineering Research Council of Canada and the University of Toronto for funding.…”

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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Computer recognition of the speech of adults with cerebral palsy and dysarthria

Cited by 33 publications

References 6 publications

Articulatory Knowledge in the Recognition of Dysarthric Speech

Articulatory Knowledge in the Recognition of Dysarthric Speech

Model adaptation and adaptive training for the recognition of dysarthric speech

Adapting acoustic and lexical models to dysarthric speech

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