APPLICATIONS OF ADAPTIVE WAVELETS FOR SPEECH

Our objective is to demonstrate the applicability of adaptive wavelets for speech applications. In particular, we discuss two applications, namely, classification of unvoiced sounds and speaker identification. First, a method to classify unvoiced sounds using adaptive wavelets, w hich would help in developing a unified algorithm to classify phonemes (speech sounds), is described. Next, the applicability of adaptive wa velets to identify speakers using very short speech data (one pitch pe riod) is exhibited. The described text-independent phoneme based speak er identification algorithm identifies a speaker by first modeling pho nemes and then by clustering all the phonemes belonging to the same sp eaker into one class. For both applications, we use feed-forward neura l network architecture. We demonstrate the performance of both unvoice d sounds classifier and speaker identification algorithms by using rep resentative real speech examples.