STATE MAPPING FOR CROSS-LANGUAGE SPEAKER ADAPTATION

摘要

Creation of sub-phonemic Hidden Markov Model (HMM) states and the mapping of those states results in improved cross-language speaker adaptation. The smaller sub-phonemic mapping provides improvements in usability and intelligibility particularly between languages with few common phonemes. HMM states of different languages may be mapped to one another using a distance between the HMM states in acoustic space. This distance may be calculated using Kullback-Leibler divergence and multi-space probability distribution. By combining distance mapping and context mapping for different speakers of the same language improved cross-language speaker adaptation is possible.