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COMPARISON OF SPEECH TASKS FOR AUTOMATIC CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS AND HEALTHY SUBJECTS

Illa, Aravind and Patel, Deep and Yamini, BK and Meera, SS and Shivashankar, N and Veeramani, Preethish-Kumar and Vengalil, Seena and Polavarapu, Kiran and Nashi, Saraswati and Nalini, Atchayaram and Ghosh, Prasanta Kumar (2018) COMPARISON OF SPEECH TASKS FOR AUTOMATIC CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS AND HEALTHY SUBJECTS. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), APR 15-20, 2018, Calgary, CANADA, pp. 6014-6018.

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Official URL: http://dx.doi.org/10.1109/ICASSP.2018.8461836

Abstract

In this work, we consider the task of acoustic and articulatory feature based automatic classification of Amyotrophic Lateral Sclerosis (ALS) patients and healthy subjects using speech tasks. In particular, we compare the roles of different types of speech tasks, namely rehearsed speech, spontaneous speech and repeated words for this purpose. Simultaneous articulatory and speech data were recorded from 8 healthy controls and 8 ALS patients using AG501 for the classification experiments. In addition to typical acoustic and articulatory features, new articulatory features are proposed for classification. As classifiers, both Deep Neural Networks (DNN) and Support Vector Machines (SVM) are examined. Classification experiments reveal that the proposed articulatory features outperform other acoustic and articulatory features using both DNN and SVM classifier. However, SVM performs better than DNN classifier using the proposed feature. Among three different speech tasks considered, the rehearsed speech was found to provide the highest F-score of 1, followed by an F-score of 0.92 when both repeated words and spontaneous speech are used for classification.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belong to IEEE
Keywords: Amyotrophic Lateral Sclerosis; Support Vector Machine; Deep Neural Networks; Electro Magnetic Articulography; Articulatory Kinematic Features
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Depositing User: Id for Latest eprints
Date Deposited: 26 Oct 2018 14:44
Last Modified: 26 Oct 2018 14:44
URI: http://eprints.iisc.ac.in/id/eprint/60966

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