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 the 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 non-linear 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 retained on telephone speech.

[1]  Alex Waibel,et al.  Noise reduction using connectionist models , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[2]  Chin-Hui Lee,et al.  Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..

[3]  Chin-Hui Lee,et al.  Unsupervised, smooth training of feed-forward neural networks for mismatch compensation , 1997, 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings.

[4]  R. Kompe,et al.  Global optimization of a neural network-hidden Markov model hybrid , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[5]  Sadaoki Furui,et al.  N-best-based instantaneous speaker adaptation method for speech recognition , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[6]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[7]  Alain Biem,et al.  Feature extraction based on minimum classification error/generalized probabilistic descent method , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Mark J. F. Gales,et al.  Robust continuous speech recognition using parallel model combination , 1996, IEEE Trans. Speech Audio Process..

[9]  Chin-Hui Lee,et al.  A maximum-likelihood approach to stochastic matching for robust speech recognition , 1996, IEEE Trans. Speech Audio Process..

[10]  Chin-Hui Lee,et al.  Simultaneous ANN feature and HMM recognizer design using string-based minimum classification error (MCE) training , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[11]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[12]  P. Woodland,et al.  Flexible speaker adaptation using maximum likelihood linear regression , 1995 .

[13]  Qiguang Lin,et al.  Environment-independent continuous speech recognition using neural networks and hidden Markov models , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[14]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[15]  James L. Flanagan,et al.  N‐best breadth search for large vocabulary continuous speech recognition using a long span language model , 1998 .

[16]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .

[17]  Mark J. F. Gales,et al.  Improving environmental robustness in large vocabulary speech recognition , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[18]  Yoshua Bengio,et al.  Global optimization of a neural network-hidden Markov model hybrid , 1992, IEEE Trans. Neural Networks.

[19]  James L. Flanagan,et al.  Robust speech recognition using maximum likelihood neural networks and continuous density hidden Markov models , 1997, 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings.

[20]  Xuedong Huang.  Speaker normalization for speech recognition , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[21]  Gérard Chollet,et al.  Robust speech parameters extraction for word recognition in noise using neural networks , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[22]  Steve J. Young,et al.  MMIE training of large vocabulary recognition systems , 1997, Speech Communication.

[23]  H.B.D. Sorensen,et al.  A cepstral noise reduction multi-layer neural network , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[24]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[25]  James L. Flanagan,et al.  Environment-Independent Continuous Speech Recognition , 1996 .

[26]  Sara H. Basson,et al.  NTIMIT: a phonetically balanced, continuous speech, telephone bandwidth speech database , 1990, International Conference on Acoustics, Speech, and Signal Processing.