Smoothed N-best-based speaker adaptation for speech recognition

Smoothed estimation and utterance veri cation are introduced into the N-best-based speaker adaptation method. That method is e ective even for speakers whose decodings using speaker-independent (SI) models are error-prone, that is, for speakers for whom adaptation techniques are truly needed. The smoothed estimation improves the performance for such speakers, and the utterance veri cation reduces the required amount of calculation. Performance evaluation using connected-digit (four-digit strings) recognition experiments performed over actual telephone lines showed a reduction of 36.4% in the error rates for speakers whose decodings using SI models are error-prone. To try and nd an e ective model-transformation for speaker adaptation, we discuss replacing mixture-mean bias estimation by the widely used mixture-mean linear-regression-matrix estimation.