Speaker-independent isolated word recognition based on emphasized spectral dynamics

A new speech analysis technique applicable to speech recognition is proposed considering the auditory mechanism of speech perception which emphasizes spectral dynamics and which compensates for the spectral undershoot associated with coarticulation. A speech wave is represented by the LPC cepstrum and logarithmic energy sequences, and the time sequences over short periods are expanded by the first- and second-order polynomial functions at every frame period. The dynamics of the cepstrum sequences are then emphasized by the linear combination of their polynomial expansion coefficients, that is, derivatives, and their instantaneous values. Speaker-independent word recognition experiments using time functions of the dynamics-emphasized cepstrum and the polynomial coefficient for energy indicate that the error rate can be largely reduced by this method.