Spectral Subband Centroid Features For Speech Recognition

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Author(s)
Paliwal, KK
Griffith University Author(s)
Year published
1998
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Cepstral coefficients derived either through linear prediction (LP) analysis or from filter banks are perhaps the most commonly used features in currently available speech recognition systems. In this paper, we propose spectral subband centroids as new features and use them as a supplement to cepstral features for speech recognition. We show that these features have properties similar to formant frequencies and they are quite robust to noise. Recognition results are reported, justifying the usefulness of these features as supplementary features.Cepstral coefficients derived either through linear prediction (LP) analysis or from filter banks are perhaps the most commonly used features in currently available speech recognition systems. In this paper, we propose spectral subband centroids as new features and use them as a supplement to cepstral features for speech recognition. We show that these features have properties similar to formant frequencies and they are quite robust to noise. Recognition results are reported, justifying the usefulness of these features as supplementary features.
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Conference Title
PROCEEDINGS OF THE 1998 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-6
Volume
2
Publisher URI
Copyright Statement
© 1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.