Title :
Research on Speaker Recognition Based on Multifractal Spectrum Feature
Author :
Zhou, Yuhuan ; Wang, Jinming ; Zhang, Xiongwei
Author_Institution :
PLA Univ. of Sci. & Technol., Nanjing, China
Abstract :
In this paper, a new nonlinear feature extraction method based on the WTMM (wavelet transform modulus-maxima method) is proposed, which can greatly facilitate the extraction of the multifractal spectrum feature (MSF) from speech signals. The MSF combined with traditional linear features can obviously improve the performance of speaker recognition system. Experiment results show that 6-dimensional MSF combined with LPC make recognition accuracy increase 6.4 percentage points, and 6-dimensional MSF combined with MFCC, LPC make recognition accuracy increase 1.6 percentage points and reach 98.8% in short speech (2 seconds) speaker recognition.
Keywords :
feature extraction; linear predictive coding; speaker recognition; spectral analysis; speech processing; wavelet transforms; multifractal spectrum feature; nonlinear feature extraction method; speaker recognition system; speech signals; wavelet transform modulus-maxima method; Data mining; Feature extraction; Fractals; Geometry; Linear predictive coding; Mel frequency cepstral coefficient; Speaker recognition; Speech analysis; Speech recognition; Wavelet transforms; LPC; MFCC; multifractal spectrum feature; speaker recognition; wavelet transform modulus-maxima method;
Conference_Titel :
Computer Modeling and Simulation, 2010. ICCMS '10. Second International Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-1-4244-5642-0
Electronic_ISBN :
978-1-4244-5643-7
DOI :
10.1109/ICCMS.2010.66
