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Comparison of AM-FM Based Features For Robust Speech Recognition

Narayana, KVS and Sreenivas, TV (2008) Comparison of AM-FM Based Features For Robust Speech Recognition. In: INTERSPEECH 2008 9th Annual Conference of the International Speech Communication Association, September 22-26, 2008, Brisbane, Australia.

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Official URL: http://www.isca-speech.org/archive/interspeech_200...

Abstract

Effective feature extraction for robust speech recognition is a widely addressed topic and currently there is much effort to invoke non-stationary signal models instead of quasi-stationary signal models leading to standard features such as LPC or MFCC. Joint amplitude modulation and frequency modulation (AM-FM) is a classical non-parametric approach to non-stationary signal modeling and recently new feature sets for automatic speech recognition (ASR) have been derived based on a multi-band AM-FM representation of the signal. We consider several of these representations and compare their performances for robust speech recognition in noise, using the AURORA-2 database. We show that FEPSTRUM representation proposed is more effective than others. We also propose an improvement to FEPSTRUM based on the Teager energy operator (TEO) and show that it can selectively outperform even FEPSTRUM

Item Type: Conference Paper
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 19 Sep 2011 08:52
Last Modified: 19 Sep 2011 08:52
URI: http://eprints.iisc.ernet.in/id/eprint/40586

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