Speech feature extraction using independent component analysis

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 reordering method. The basis functions are almost ordered from low frequency basis vector to high frequency basis vector. And this is compatible with the fact that human speech signals have much more information on 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.