Automatic selection of speakers for improved acoustic modelling: recognition of disordered speech with sparse data

Christensen, Heidi; Casanueva, Iñigo; Cunningham, Stuart; Green, Phil; Hain, Thomas

doi:10.1109/slt.2014.7078583

Cited by 22 publications

(17 citation statements)

References 20 publications

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“…While traditional, off-theshelf Automatic Speech Recognition (ASR) systems perform well for normal speech, this is not the case with the atypical dysarthric speech owing to the inter-speaker and intra-speaker inconsistencies in the acoustic space as well as the sparseness of data. Several techniques are employed to improve ASR performance for dysarthric speech: acoustic space enhancement, feature engineering, Deep Neural Networks (DNN), speaker adaptation, lexical model adaptation-individually or as a combination thereof [2,3,4,5,6]. In order to exploit the machine learning techniques for ASR fully, suitable data to build these systems is imperative.…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation Using Healthy Speech for Dysarthric Speech Recognition

Vachhani¹,

Bhat²,

Kopparapu³

2018

Interspeech 2018

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Dysarthria refers to a speech disorder caused by trauma to the brain areas concerned with motor aspects of speech giving rise to effortful, slow, slurred or prosodically abnormal speech. Traditional Automatic Speech Recognizers (ASR) perform poorly on dysarthric speech recognition tasks, owing mostly to insufficient dysarthric speech data. Speaker related challenges complicates data collection process for dysarthric speech. In this paper, we explore data augmentation using temporal and speed modifications to healthy speech to simulate dysarthric speech. DNN-HMM based Automatic Speech Recognition (ASR) and Random Forest based classification were used for evaluation of the proposed method. Dysarthric speech, generated synthetically, is classified for severity level using a Random Forest classifier that is trained on actual dysarthric speech. ASR trained on healthy speech, augmented with simulated dysarthric speech is evaluated for dysarthric speech recognition. All evaluations were carried out using Universal Access dysarthric speech corpus. An absolute improvement of 4.24% and 2% WAS achieved using tempo based and speed based data augmentation respectively as compared to ASR performance using healthy speech alone for training.

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Section: Introductionmentioning

confidence: 99%

Data Augmentation Using Healthy Speech for Dysarthric Speech Recognition

Vachhani¹,

Bhat²,

Kopparapu³

2018

Interspeech 2018

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“…In [10], authors propose a DNN based interpretable model for objective assessment of dysarthric speech that provides users with an estimate of severity as well as a set of explanatory features. Speaker selection and speaker adaptation techniques have been employed to improve ASR performance for dysarthric speech in [11,12]. ASR configurations have been designed and optimized using dysarthria severity level cues in [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Dysarthric Speech Recognition Using Time-delay Neural Network Based Denoising Autoencoder

et al. 2018

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Dysarthria is a manisfestation of the disruption in the neuromuscular physiology resulting in uneven, slow, slurred, harsh or quiet speech. Dysarthric speech poses serious challenges to automatic speech recognition, considering this speech is difficult to decipher for both humans and machines. The objective of this work is to enhance dysarthric speech features to match that of healthy control speech. We use a Time-Delay Neural Network based Denoising Autoencoder (TDNN-DAE) to enhance the dysarthric speech features. The dysarthric speech thus enhanced is recognized using a DNN-HMM based Automatic Speech Recognition (ASR) engine. This methodology was evaluated for speaker-independent (SI) and speaker-adapted (SA) systems. Absolute improvements of 13% and 3% was observed in the ASR performance for SI and SA systems respectively as compared with unenhanced dysarthric speech recognition.

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“…In [2], a similarity measure between dysarthric speakers to select relevant speaker data for training rather than speaker independent acoustic models, followed by maximum a posteriori (MAP) adaptation has been used. In [3] a more suitable prior model for adaptation based on the dysarthric speaker's acoustic characteristics has been used to achieve improved recognition.…”

Section: Introductionmentioning

confidence: 99%

Deep Autoencoder Based Speech Features for Improved Dysarthric Speech Recognition

et al. 2017

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Dysarthria is a motor speech disorder, resulting in mumbled, slurred or slow speech that is generally difficult to understand by both humans and machines. Traditional Automatic Speech Recognizers (ASR) perform poorly on dysarthric speech recognition tasks. In this paper, we propose the use of deep autoencoders to enhance the Mel Frequency Cepstral Coefficients (MFCC) based features in order to improve dysarthric speech recognition. Speech from healthy control speakers is used to train an autoencoder which is in turn used to obtain improved feature representation for dysarthric speech. Additionally, we analyze the use of severity based tempo adaptation followed by autoencoder based speech feature enhancement. All evaluations were carried out on Universal Access dysarthric speech corpus. An overall absolute improvement of 16% was achieved using tempo adaptation followed by autoencoder based speech front end representation for DNN-HMM based dysarthric speech recognition.

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Automatic selection of speakers for improved acoustic modelling: recognition of disordered speech with sparse data

Cited by 22 publications

References 20 publications

Data Augmentation Using Healthy Speech for Dysarthric Speech Recognition

Data Augmentation Using Healthy Speech for Dysarthric Speech Recognition

Dysarthric Speech Recognition Using Time-delay Neural Network Based Denoising Autoencoder

Deep Autoencoder Based Speech Features for Improved Dysarthric Speech Recognition

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