Improving Rapid Unsupervised Speaker Adaptation Based On Hmm Sufficient Statistics

In real-time speech recognition applications, there is a need to implement a fast and reliable adaptation algorithm. We propose a method to reduce adaptation time of the unsupervised speaker adaptation based on HMM-sufficient statistics. We use only a single arbitrary utterance without transcriptions in selecting the N-best speakers' sufficient statistics created offline to provide data for adaptation to a target speaker. Further reduction of N-best implies a reduction in adaptation time. However, it degrades recognition performance due to insufficiency of data needed to robustly adapt the model. Linear interpolation of the global HMM-sufficient statistics offsets this negative effect and achieves a 50% reduction in adaptation time without compromising the recognition performance. We have reduced the adaptation time from 10 sec to 5 sec without degradation of the word accuracy. Furthermore, we compared our method with vocal tract length normalization (VTLN), maximum a posteriori (MAP) and maximum likelihood linear regression (MLLR). Moreover, we tested in office, car, crowd and booth noise environments in 10 dB, 15 dB, 20 dB and 25 dB SNRs