An Exploration of Log-Mel Spectrogram and MFCC Features for Alzheimer's Dementia Recognition from Spontaneous Speech

Meghanani, A and C S, A and Ramakrishnan, AG (2021) An Exploration of Log-Mel Spectrogram and MFCC Features for Alzheimer's Dementia Recognition from Spontaneous Speech. In: 2021 IEEE Spoken Language Technology Workshop, SLT 2021, 19-22 Jan 2021, Shenzhen, China, pp. 670-677.

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Abstract

In this work, we explore the effectiveness of log-Mel spectrogram and MFCC features for Alzheimer's dementia (AD) recognition on ADReSS challenge dataset. We use three different deep neural networks (DNN) for AD recognition and mini-mental state examination (MMSE) score prediction: (i) convolutional neural network followed by a long-short term memory network (CNN-LSTM), (ii) pre-trained ResNet18 network followed by LSTM (ResNet-LSTM), and (iii) pyramidal bidirectional LSTM followed by a CNN (pBLSTM-CNN). CNN-LSTM achieves an accuracy of 64.58 with MFCC features and ResNet-LSTM achieves an accuracy of 62.5 using log-Mel spectrograms. pBLSTM-CNN and ResNet-LSTM models achieve root mean square errors (RMSE) of 5.9 and 5.98 in the MMSE score prediction, using the log-Mel spectrograms. Our results beat the baseline accuracy (62.5) and RMSE (6.14) reported for acoustic features on ADReSS challenge dataset. The results suggest that log-Mel spectrograms and MFCCs are effective features for AD recognition problem when used with DNN models. © 2021 IEEE.

Item Type:	Conference Proceedings
Publication:	2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
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; Deep neural networks; Mean square error; Neurodegenerative diseases; Spectrographs; Speech recognition, Acoustic features; Alzheimer's dementia; Baseline accuracy; Mini-mental state examinations; Root mean square errors; Short term memory; Spectrograms; Spontaneous speech, Long short-term memory
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	13 Jul 2021 08:49
Last Modified:	13 Jul 2021 08:49
URI:	http://eprints.iisc.ac.in/id/eprint/68815

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