Recognition of noisy speech using dynamic spectral subband centroids

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Author(s)
Chen, JD
Huang, YT
Li, Q
Paliwal, KK
Griffith University Author(s)
Year published
2004
Metadata
Show full item recordAbstract
Despite their widespread popularity as front-end parameters for speech recognition, the cepstral coefficients derived from either linear prediction analysis or a filter-bank are found to be sensitive to additive noise. In this letter, we discuss the use of spectral subband centroids for robust speech recognition. We show that centroids, if properly selected, can achieve recognition performance comparable to that of the mel-frequency cepstral coefficients (MFCCs) in clean speech, while delivering better performance than MFCC in noisy environments. A procedure is proposed to construct the dynamic centroid feature vector that ...
View more >Despite their widespread popularity as front-end parameters for speech recognition, the cepstral coefficients derived from either linear prediction analysis or a filter-bank are found to be sensitive to additive noise. In this letter, we discuss the use of spectral subband centroids for robust speech recognition. We show that centroids, if properly selected, can achieve recognition performance comparable to that of the mel-frequency cepstral coefficients (MFCCs) in clean speech, while delivering better performance than MFCC in noisy environments. A procedure is proposed to construct the dynamic centroid feature vector that essentially embodies the transitional spectral information. We discuss some properties of the proposed dynamic features.
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View more >Despite their widespread popularity as front-end parameters for speech recognition, the cepstral coefficients derived from either linear prediction analysis or a filter-bank are found to be sensitive to additive noise. In this letter, we discuss the use of spectral subband centroids for robust speech recognition. We show that centroids, if properly selected, can achieve recognition performance comparable to that of the mel-frequency cepstral coefficients (MFCCs) in clean speech, while delivering better performance than MFCC in noisy environments. A procedure is proposed to construct the dynamic centroid feature vector that essentially embodies the transitional spectral information. We discuss some properties of the proposed dynamic features.
View less >
Journal Title
IEEE Signal Processing Letters
Volume
11
Issue
2
Copyright Statement
© 2004 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.
Subject
Communications engineering