Speech and Vision Lab

Quest for new speaker dependent features is a constant problem in the design of automatic speaker recognition systems. In speech, information about the speaker usually arises along with the semantic information which makes its independent use difficult. In this paper, a method based on linear prediction (LP) analysis is described which yields features that are more speaker dependent than the usual linear predictor coefficients (LPC). In this method the LPC contours are obtained through cascade realization of digital inverse filtering (DIF) for speech signals. A low order (2-4) DIF removes the gross spectral characteristics such as the large dynamic range and some significant peaks which tend to mask the weaker formants. Visual comparison of the contours and a preliminary statistical analysis indicate that the LPC contours obtained by processing the output signal of the first stage contain better features for speaker dependency than the direct LPC contours.