Applying Independent Component Analysis for Speech Feature Detection
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چکیده
An approach to speech feature detection is developed, which uses the technique of independent component analysis for a blind (unsupervised learning) detection of basic vectors in the Fourier space. This kind of features could replace the Mel Frequency Cepstrum Coefficient (MFCC) features, widely used today for phoneme-based speech recognition. Alternatively, the ICA components could act as basic features in speaker verification systems.
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تاریخ انتشار 2004