Speech task based automatic classification of ALS and Parkinson's Disease and their severity using log Mel spectrograms

Suhas, BN and Mallela, J and Illa, A and Yamini, BK and Atchayaram, N and Yadav, R and Gope, D and Ghosh, PK (2020) Speech task based automatic classification of ALS and Parkinson's Disease and their severity using log Mel spectrograms. In: SPCOM 2020 - International Conference on Signal Processing and Communications, 19 - 24 July 2020, Bangalore.

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Abstract

We consider the task of speech based classification of patients with amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD) and healthy controls (HC). Recent work in convolutional neural networks (CNN) to solve image classification problems raises the possibility of utilizing spectral representation of speech for detection of neurological diseases. In this paper, a spectrogram based approach is used. Feeding overlapping windows to the CNN makes sure that the temporal aspects are considered by using short signal segments or wide analysis filters. A three class (ALS, PD or HC) dysarthria classification is performed. In addition, we perform two severity classification experiments for ALS (5 class) and PD (3 class) respectively. Experiments are conducted on both baseline MFCC data 1 and log Mel spectrograms. Classification results show that for several audio lengths, models trained on log Mel spectrograms consistently outperform those of MFCC's. The ability of the network to accurately classify different classes is evaluated via the area under receiver operating characteristic curve 2,3. The findings from this study could aid in better detection and monitoring of ALS and PD diseases. © 2020 IEEE.

Item Type:	Conference Paper
Publication:	SPCOM 2020 - International Conference on Signal Processing and Communications
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Convolutional neural networks; Signal processing; Spectrographs, Amyotrophic lateral sclerosis; Automatic classification; Classification results; Neurological disease; Overlapping window; Parkinson's disease; Receiver operating characteristic curves; Spectral representations, Neurodegenerative diseases
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	06 Feb 2023 06:18
Last Modified:	06 Feb 2023 06:18
URI:	https://eprints.iisc.ac.in/id/eprint/79847

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