Acoustic model adaptation using in-domain background models for dysarthric speech recognition

Sharma, Harsh; Hasegawa‐Johnson, Mark

doi:10.1016/j.csl.2012.10.002

Cited by 47 publications

(48 citation statements)

References 17 publications

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“…For instance, these results suggest that the variabilities in dysarthric speech can be better accommodated from modelling both typical and dysarthric domains. One such attempt was reported by [28], where background interpolation MAP was implemented to obtain an intermediate prior acoustic model to narrow the gap between two disparate SI systems (typical & dysarthric), albeit, the reported results were no better than those reported by [21]. Our best overall results, as reported in sections 3.3 & 3.4, are based on MLLR-MAP adapted SAT systems.…”

Section: Discussionsupporting

confidence: 67%

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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Section: Discussionsupporting

confidence: 67%

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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“…When comparing the more recent researches of Morales and Romero (2014), we found that the correlation of speech intelligibility in the recognition accuracy of ASR system was lower than the previous research by Sharma and Hasegawa-Johnson (2012), with both of them uses Nemours speech database. This may indicates that new method such as the one in Morales and Romero (2014) may have improved the recognition accuracy of the ASR system in recognising dysarthric speech with low intelligibility.…”

Section: Specific Factorscontrasting

confidence: 76%

Exploring the influence of general and specific factors on the recognition accuracy of an ASR system for dysarthric speaker

Mustafa

Rosdi

Salim

et al. 2015

Expert Systems with Applications

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“…For example, Parkinson's disease and amyotrophic lateral sclerosis (ALS) impact the patient's motor functions and therefore impair their speech. Only a few studies have been focused on dysarthric speech recognition [4][5][6]. Recent studies using mixed data from a variety of neurological diseases indicated articulatory data can improve the speech recognition performance [7,8].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Dysarthric Speech due to Amyotrophic Lateral Sclerosis with Across-Speaker Articulatory Normalization

Hahm¹,

Heitzman²,

Wang³

2015

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

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Recent dysarthric speech recognition studies using mixed data from a collection of neurological diseases suggested articulatory data can help to improve the speech recognition performance. This project was specifically designed for the speakerindependent recognition of dysarthric speech due to amyotrophic lateral sclerosis (ALS) using articulatory data. In this paper, we investigated three across-speaker normalization approaches in acoustic, articulatory, and both spaces: Procrustes matching (a physiological approach in articulatory space), vocal tract length normalization (a data-driven approach in acoustic space), and feature space maximum likelihood linear regression (a model-based approach for both spaces), to address the issue of high degree of variation of articulation across different speakers. A preliminary ALS data set was collected and used to evaluate the approaches. Two recognizers, Gaussian mixture model (GMM) -hidden Markov model (HMM) and deep neural network (DNN) -HMM, were used. Experimental results showed adding articulatory data significantly reduced the phoneme error rates (PERs) using any or combined normalization approaches. DNN-HMM outperformed GMM-HMM in all configurations. The best performance (30.7% PER) was obtained by triphone DNN-HMM + acoustic and articulatory data + all three normalization approaches, a 15.3% absolute PER reduction from the baseline using triphone GMM-HMM + acoustic data.

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Acoustic model adaptation using in-domain background models for dysarthric speech recognition

Cited by 47 publications

References 17 publications

Model adaptation and adaptive training for the recognition of dysarthric speech

Model adaptation and adaptive training for the recognition of dysarthric speech

Exploring the influence of general and specific factors on the recognition accuracy of an ASR system for dysarthric speaker

Recognizing Dysarthric Speech due to Amyotrophic Lateral Sclerosis with Across-Speaker Articulatory Normalization

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