On the time variability of vocal tract for speaker recognition

A novel scheme to analyze the effects of time variability of vocal tract for speaker recognition is proposed. We adopt a pitch synchronous feature extraction method to describe even more detailed characteristics of vocal tract, and decompose it into rapidly varying and slowly varying components with a specified linear filter along with time axis. Speaker identification tasks are performed with weighted combination of two decomposed feature sets and their corresponding models to show the efficiency of each decomposed feature set. Simulation results show that slowly varying components contain more speaker discriminative information than rapidly varying components do.