Data-driven codebook adaptation in phonetically tied SCHMMs

This paper reports the results of our experiments aimed at the automatic optimization of the number of parameters in the semi-continuous phonetically tied HMM based speech recognition system that is part of the speech-to-speech translation system JANUS-2. We propose different algorithms devised to determine the optimal number of model parameters. In recognition experiments performed on a spontaneous human-to-human dialog database, we show that automatic optimization of the acoustic modeling parameter size with the proposed algorithm improves the recognition performance without increasing the required amount of computing power and memory