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Machine Learning Based Assistive Speech Technology for People with Neurological Disorders

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Recent Advances in Intelligent Assistive Technologies: Paradigms and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 170))

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Abstract

With the tremendous improvements of automatic speech recognition systems worldwide, efficient ways of recognizing dysarthric speech has emerged as a practical challenge. Recognizing the impaired speech with poor articulation, missing consonants, and so forth is one of the foremost requirements in research for speech domain. Given an unknown dysarthric (partial) speech utterance, the problem is to recognize the speech content. I first review and analyze the different approaches such as generative, discriminative, hybrid model based approaches and unsupervised approaches for dysarthric speech recognition (DSR). Next, I present a framework in which effective representations are formed using generative model-driven features for dysarthric speech recognition task. The performance of the proposed method is examined to recognize the isolated utterances from the UA-Speech database. The recognition accuracy of the proposed approach is better than the conventional hidden Markov model-based approach.

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Authors and Affiliations

  1. Intelligent Systems Group, School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India

    Shanmuganathan Chandrakala

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  1. Shanmuganathan Chandrakala
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Correspondence to Shanmuganathan Chandrakala .

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Editors and Affiliations

  1. Faculty of Medical Bioengineering, University of Medicine and Pharmacy, Iași, Romania

    Hariton Costin

  2. University of Augsburg, Augsburg, Germany

    Björn Schuller

  3. Faculty of Automatic Control and Computers, University Polytechnica of Bucharest, Bucharest, Romania

    Adina Magda Florea

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Chandrakala, S. (2020). Machine Learning Based Assistive Speech Technology for People with Neurological Disorders. In: Costin, H., Schuller, B., Florea, A. (eds) Recent Advances in Intelligent Assistive Technologies: Paradigms and Applications. Intelligent Systems Reference Library, vol 170. Springer, Cham. https://doi.org/10.1007/978-3-030-30817-9_6

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