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Generative Model-Driven Feature Learning for dysarthric speech recognition

Paper ID Volume ID Publish Year Pages File Format Full-Text
5098 340 2016 9 PDF Available
Title
Generative Model-Driven Feature Learning for dysarthric speech recognition
Abstract

Recognition of speech uttered by severe dysarthric speakers needs a robust learning technique. One of the commonly used generative model-based classifiers for speech recognition is a hidden Markov model. Generative model-based classifiers do not do well for overlapping classes and due to insufficient training data. Dysarthric speech is normally partial or incomplete that leads to improper learning of temporal dynamics. To overcome these issues, we focus on learning features for dysarthric speech recognition that involves recognizing the sequential patterns of varying length utterances. We propose a Generative Model-Driven Feature Learning based discriminative framework that maps the sequence of feature vectors to fixed dimension vector spaces induced by the generative models. The discriminative classifier is built in that vector space. The proposed HMM-based fixed dimensional vector representation provides better discrimination for dysarthric speech than the conventional HMM. We examine the performance of the proposed method to recognize the isolated utterances from the UA-Speech database. The recognition accuracy of the proposed model is better than the conventional hidden Markov model-based approach.

Keywords
Generative Model-Driven Feature Learning; Dysarthric speech recognition; Support vector machine; Varying length sequences; Feature vector representation
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biocybernetics and Biomedical Engineering - Volume 36, Issue 4, 2016, Pages 553–561
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
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Full-text PDF Download
Online Support
Any Questions? feel free to contact us