Speaker normalization based on frequency warping

In speech recognition, speaker-dependence of a speech recognition system comes from speaker-dependence of the speech feature, and the variation of vocal tract shape is the major source of inter-speaker variations of the speech feature, though there are some other sources which also contribute. In this paper, we address the approach of speaker normalization which aims at normalizing speaker's vocal tract length based on frequency warping (FWP). The FWP is implemented in the front-end preprocessing of our speech recognition system. We investigate the formant-based and ML-based FWP in linear and nonlinear warping modes, and compare them in detail. All experimental results are based on our JANUS3 large vocabulary continuous speech recognition system and the Spanish Spontaneous Scheduling Task database (SSST).