关键词: 语音识别, 声音效果, 自适应, 最大似然线性回归

Abstract: Adaptation of acoustic models is presented to cope with the acoustic variability caused vocal effort variability in Mandarin speech recognition. Acoustic models trained on normal speech are applied to recognize sentences under the remaining four vocal effort modes. The maximum likelihood linear regression adaptation method is extended to the stochastic segment model, and the acoustic models after adaptation are used to recognize speech of corresponding vocal effort mode. Experiments conducted on “863-test” show that there is significant?decrease in recognition accuracy in case of mismatched speech models, and the recognition performance can be improved considerably by adaptation. This proves that adaptation of acoustic models is effective in solving the acoustic variability caused vocal effort.

Key words: speech recognition, vocal effort, adaptation, maximum likelihood linear regression