A segment-based speaker adaptation neural network applied to continuous speech recognition

The authors describe a speaker adaptation technique using segment-based neural-mapping applied to continuous speech recognition. The adaptation neural network has a time-shifted subconnection architecture to maintain the temporal structure in the acoustic segment and to decrease the amount of speech data for training. The effectiveness of this network has been reported for phoneme recognition. The speaker adaptation network is combined with a TDNN-LR continuous speech recognizer, and is evaluated in word and phrase recognition experiments with several speakers. The results of 500-word recognition experiments show that the recognition rate by segment-based adaptation is 92.2%, 28.8% higher than the rate without adaptation. The results of 278 phrase recognition experiments show that the recognition rate by segment-based adaptation is 57.4%, 27.7% higher than the rate without adaptation