Abstract
The paper proposes the sub-band main peak frequencies( SMPF) for speaker identification (SI). The SMPF could be derived from the sub-band first formant frequencies by all-pole model of speech signal. Compared with MFCC features for SI based on a Gaussian mixture model (GMM), only SMPF features for SI is better than only the MFCC, with one of improved relative rate up to 15%. Experimental utterances are Chinese mandarin under clean background recording circumstances.
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Hou, L., Xie, J., Xie, S. (2011). Sub-band Main Peak Frequency Application for Speaker Identification. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, vol 7098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25449-9_23
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DOI: https://doi.org/10.1007/978-3-642-25449-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25448-2
Online ISBN: 978-3-642-25449-9
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