NDLI: Dysarthric speech recognition using a convolutive bottleneck network

Content Provider	IEEE Xplore Digital Library
Author	Nakashika, Toru Yoshioka, Toshiya Takiguchi, Tetsuya Ariki, Yasuo Duffner, Stefan Garcia, Christophe
Copyright Year	2014
Description	Author affiliation: Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR 5205, FRANCE (Duffner, Stefan; Garcia, Christophe) \|\| Graduate School of System Informatics, Kobe University, JAPAN (Nakashika, Toru; Yoshioka, Toshiya; Takiguchi, Tetsuya; Ariki, Yasuo)
Abstract	In this paper, we investigate the recognition of speech produced by a person with an articulation disorder resulting from athetoid cerebral palsy. The articulation of the first spoken words tends to become unstable due to strain on speech muscles, and that causes a degradation of traditional speech recognition systems. Therefore, we propose a robust feature extraction method using a convolutive bottleneck network (CBN) instead of the well-known MFCC. The CBN stacks multiple various types of layers, such as a convolution layer, a subsampling layer, and a bottleneck layer, forming a deep network. Applying the CBN to feature extraction for dysarthric speech, we expect that the CBN will reduce the influence of the unstable speaking style caused by the athetoid symptoms. We confirmed its effectiveness through word-recognition experiments, where the CBN-based feature extraction method outperformed the conventional feature extraction method.
Starting Page	505
Ending Page	509
File Size	891033
Page Count	5
File Format	PDF
ISBN	9781479921881
ISSN	21645221
e-ISBN	9781479921867
DOI	10.1109/ICOSP.2014.7015056
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Speech Convolution Speech recognition Accuracy Mel frequency cepstral coefficient Robustness dysarthric speech Articulation disorders feature extraction convolutional neural network bottleneck feature
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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