Telephone speech recognition using neural networks and hidden Markov models

The performance of well-trained speech recognizers using high quality full bandwidth speech data is usually degraded when used in real world environments. In particular, telephone speech recognition is extremely difficult due to the limited bandwidth of transmission channels. In this paper, neural network based adaptation methods are applied to telephone speech recognition and a new unsupervised model adaptation method is proposed. The advantage of the neural network based approach is that the retraining of speech recognizers for telephone speech is avoided. Furthermore, because the multi-layer neural network is able to compute nonlinear functions, it can accommodate for the nonlinear mapping between full bandwidth speech and telephone speech. The new unsupervised model adaptation method does not require transcriptions and can be used with the neural networks. Experimental results on TIMIT/NTIMIT corpora show that the performance of the proposed methods is comparable to that of recognizers retrained on telephone speech.