Utterance dependent parametric warping for a talker-independent HMM-based recognizer

In an effort to improve recognition performance of talker-independent speech systems, many adaptive methods have been proposed. The methods generally seek to exploit the higher recognition performance rate of talker-dependent systems and extend it to talker-independent systems. This is achieved by some form of placing talkers into several categories, usually using gender or vocal-tract size. In this paper we investigate a similar idea, but categorize each utterance independently. An utterance is processed using several spectral compressions, and the compression with the maximum likelihood is then used to train a better model. For testing, the spectral compression with the maximum likelihood is used to decode the utterance. While the spectral compressions divided the utterances well, this did not translate into significant improvement in performance, and the computational cost increase was significant.