Title :
Speech feature extraction using independent component analysis
Author :
Lee, Jong-Hwan ; Jung, Ho-Young ; Lee, Te Won ; Lee, Soo-Young
Author_Institution :
Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Taejon, South Korea
Abstract :
In this paper, we proposed new speech features using independent component analysis to human speeches. When independent component analysis is applied to speech signals for efficient encoding the adapted basis functions resemble Gabor-like features. Trained basis functions have some redundancies, so we select some of the basis functions by the reordering method. The basis functions are almost ordered from the low frequency basis vector to the high frequency basis vector. And this is compatible with the fact that human speech signals have much more information in the low frequency range. Those features can be used in automatic speech recognition systems and the proposed method gives much better recognition rates than conventional mel-frequency cepstral features
Keywords :
feature extraction; speech recognition; Gabor-like features; adapted basis functions; automatic speech recognition systems; basis functions; encoding; high frequency basis vector; human speeches; independent component analysis; low frequency basis vector; recognition rates; redundancies; reordering method; speech feature extraction; speech signals; trained basis functions; Band pass filters; Biomembranes; Feature extraction; Filter bank; Humans; Independent component analysis; Mel frequency cepstral coefficient; Speech analysis; Speech recognition; Vectors;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on
Conference_Location :
Istanbul
Print_ISBN :
0-7803-6293-4
DOI :
10.1109/ICASSP.2000.862023