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Unsupervised modulation filter learning for noise-robust speech recognition

Agrawal, Purvi and Ganapathy, Sriram (2017) Unsupervised modulation filter learning for noise-robust speech recognition. In: JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 142 (3). pp. 1686-1692.

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Official URL: http://dx.doi.org/10.1121/1.5001926


The modulation filtering approach to robust automatic speech recognition (ASR) is based on enhancing perceptually relevant regions of the modulation spectrum while suppressing the regions susceptible to noise. In this paper, a data- driven unsupervised modulation filter learning scheme is proposed using convolutional restricted Boltzmann machine. The initial filter is learned using the speech spectrogram while subsequent filters are learned using residual spectrograms. The modulation filtered spectrograms are used for ASR experiments on noisy and reverberant speech where these features provide significant improvements over other robust features. Furthermore, the application of the proposed method for semi- supervised learning is investigated. (C) 2017 Acoustical Society of America.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the ACOUSTICAL SOC AMER AMER INST PHYSICS, STE 1 NO 1, 2 HUNTINGTON QUADRANGLE, MELVILLE, NY 11747-4502 USA
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 23 Dec 2017 09:00
Last Modified: 23 Dec 2017 09:00
URI: http://eprints.iisc.ac.in/id/eprint/58484

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