Speaker adaptation based on judge network with small adaptation words

In this paper we report a speaker adaptation method when a small subset of word classes is available for the adaptation. A spectral transformation approach is used to adapt to a new speaker without changing the parameters of speaker-independent recognizer. By using “judge” network the degradation of the recognition rates for non-adapted word classes is minimized, which leads to the improvement of overall word recognition rates. The pruned judge network uses less parameters, but shows better generalization capability than full connected linear judge networks. Remarkable reduction of error rates is achieved for adapted 10 words, while maintaining almost same recognition rates for the non-adapted words. The results demonstrated much better recognition rates compared to the base-line system