Optimal quantization by matrix searching
Journal of Algorithms
Vector quantization and signal compression
Vector quantization and signal compression
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
Robust speech recognition in noisy environments based on subband spectral centroid histograms
IEEE Transactions on Audio, Speech, and Language Processing
An overview of text-independent speaker recognition: From features to supervectors
Speech Communication
Comparison of the impact of some Minkowski metrics on VQ/GMM based speaker recognition
Computers and Electrical Engineering
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Spectral subband centroids (SSC) have been used as an additional feature to cepstral coefficients in speech and speaker recognition. SSCs are computed as the centroid frequencies of subbands and they capture the dominant frequencies of the short-term spectrum. In the baseline SSC method, the subband filters are pre-specified. To allow better adaptation to formant movements and other dynamic phenomena, we propose to adapt the subband filter boundaries on a frame-by-frame basis using a globally optimal scalar quantization scheme. The method has only one control parameter, the number of subbands. Speaker verification results on the NIST 2001 task indicate that the selection of the parameter is not critical and that the method does not require additional feature normalization.