Noise independent speech recognition for a variety of noise types

By a base transformation technique, we previously reported a recognizer which gives a noise-adapted recognition rate of 90% under 10 dB SNR on a vocabulary of 206 words. This rate is 97% of the recognition rate for clean speech. The technique is extended here so that the input noise is first recognized as one of a set reference noises, and the noise reference is used for the base transformation of the noisy utterance. Using 32 reference noise classes, for speech signals corrupted by noises of unknown natures (either Gaussian, bus or aircrafts with SNR randomly from 10 to 40 dB), we obtained a noise-independent recognition rate of about 95.5% of the recognition rate for clean speech.<<ETX>>

[1]  Biing-Hwang Juang,et al.  Signal restoration by spectral mapping , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Yifan Gong,et al.  Base transformation for environment adaptation in continuous speech recognition , 1993, EUROSPEECH.

[3]  Yifan Gong,et al.  Stochastic trajectory modeling for speech recognition , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Thomas F. Quatieri,et al.  Noise reduction using a soft-decision sine-wave vector quantizer , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[5]  D. O'Shaughnessy,et al.  Speech enhancement using vector quantization and a formant distance measure , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[6]  J.-P. Haton,et al.  Continuous speech recognition based on high plausibility regions , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[7]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..