Filtering the time sequence of spectral parameters for speaker-independent CDHMM word recognition.

Abstract

In this work, we show how speaker-independent CDHMM word recognition performance can be significantly improved for clean speech by filtering the time sequence of spectral parameters to enhance its time dynamics. Experimental results with the standard TI connected digits database show the filter can achieve more than 30% reduction of string recognition error. As shown in this paper, that improvement is partially due to the speaker variability reduction obtained by attenuating the very low modulation frequencies. The widely used cepstral mean subtraction technique also improves the recognition rate, but it can not achieve such a noticeable improvement as the parameter filter. In fact, the best results are obtained when the peak of the long-term spectrum of the filter output is at around 3 Hz, a frequency which corresponds to the average syllable rate of the employed database.