Speaker adaptation using improved speaker Markov models

An attempt has been made to develop improved and more sophisticated SMMs (speaker Markov models) capable of modeling the acoustic differences between two speakers in a more accurate way, thus leading to improved recognition rates for the adapted speech recognition system. The original SSM approach has been improved by the introduction of the following three features: the use of fenonic speaker Markov models, the introduction of phoneme-dependent SMM parameters, and the use of special weighting between the short original training data of the new speaker and the adapted training data of the reference speaker. It was found that the phoneme recognition performance of these improved SMMs can be more than twice as high as the performance of the original SMM approach, which has already led to satisfying adaptation results for a large-vocabulary speech recognition task.<>