Feature extraction from higher-lag autocorrelation coefficients for robust speech recognition

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Title Feature extraction from higher-lag autocorrelation coefficients for robust speech recognition
Author Shannon, Ben James; Paliwal, Kuldip Kumar
Journal Name Speech Communication
Year Published 2006
Place of publication Netherlands
Publisher Elsevier BV
Abstract In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower-time lags, while the higher-lag autocorrelation coefficients are least affected, this method discards the lower-lag autocorrelation coefficients and uses only the higher-lag autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-lag autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power spectral estimate is processed further (like the well-known Mel frequency cepstral coefficient (MFCC) procedure) by the Mel filter bank, log operation and the discrete cosine transform to get the cepstral coefficients. These cepstral coefficients are referred to as the autocorrelation Mel frequency cepstral coefficients (AMFCCs). We evaluate the speech recognition performance of the AMFCC features on the Aurora and the resource management databases and show that they perform as well as the MFCC features for clean speech and their recognition performance is better than the MFCC features for noisy speech. Finally, we show that the AMFCC features perform better than the features derived from the robust linear prediction-based methods for noisy speech.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.elsevier.com/wps/find/journaldescription.cws_home/505597/description#description
Alternative URI http://dx.doi.org/10.1016/j.specom.2006.08.003
Volume 48
Page from 1458
Page to 1485
ISSN 0167-6393
Date Accessioned 2007-03-18
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Speech Recognition
URI http://hdl.handle.net/10072/14344
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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