Recent Developments in Robust Speech Recognition

Robust speech recognition refers to the problem of designing an automatic speech recognizer that works well in a wide range of unexpected or adverse environments. As the technology of automatic speech recognition moves out of the laboratories into field applications, the issue of robustness becomes a key element that distinguishes a successful deployment from a failed one.

[1]  Fumitada Itakura,et al.  Distance measure for speech recognition based on the smoothed group delay spectrum , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Douglas A. Reynolds,et al.  Integrated models of signal and background with application to speaker identification in noise , 1994, IEEE Trans. Speech Audio Process..

[3]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[4]  T. Martin,et al.  On the effects of varying filter bank parameters on isolated word recognition , 1982 .

[5]  J. Junqua,et al.  Acoustic and perceptual studies of Lombard speech: application to isolated-words automatic speech recognition , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[6]  Alejandro Acero,et al.  Acoustical and environmental robustness in automatic speech recognition , 1991 .

[7]  A. Sankar,et al.  Stochastic matching for robust speech recognition , 1994, IEEE Signal Processing Letters.

[8]  Dennis H. Klatt,et al.  A digital filter bank for spectral matching , 1976, ICASSP.

[9]  P. Darlington,et al.  Adaptive noise reduction in aircraft communication systems , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  H. Wakita,et al.  A comparative study of cepstral lifters and distance measures for all pole models of speech in noise , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[11]  Stephen Cox,et al.  Unsupervised speaker adaptation by probabilistic spectrum fitting , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[12]  Brian A. Hanson,et al.  Spectral slope distance measures with linear prediction analysis for word recognition in noise , 1987, IEEE Trans. Acoust. Speech Signal Process..

[13]  B. J. Stanton,et al.  Acoustic-phonetic analysis of loud and Lombard speech in simulated cockpit conditions , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[14]  Biing-Hwang Juang,et al.  A family of distortion measures base upon projection operation for robust speech recognition , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[15]  Richard M. Stern,et al.  Environmental robustness in automatic speech recognition , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[16]  David B. Pisoni,et al.  Some acoustic-phonetic correlates of speech produced in noise , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  N. Sedgwick,et al.  Noise compensation for speech recognition using probabilistic models , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[18]  Biing-Hwang Juang,et al.  Signal bias removal for robust telephone based speech recognition in adverse environments , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[19]  A. Nadas,et al.  Speech recognition using noise-adaptive prototypes , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[20]  Dirk Van Compernolle.  Noise adaptation in a hidden Markov model speech recognition system , 1989 .

[21]  Yariv Ephraim,et al.  A linear predictive front-end processor for speech recognition in noisy environments , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[22]  Ted H. Applebaum,et al.  Subband or cepstral domain filtering for recognition of Lombard and channel-distorted speech , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[23]  Biing-Hwang Juang,et al.  On the application of hidden Markov models for enhancing noisy speech , 1989, IEEE Trans. Acoust. Speech Signal Process..

[24]  Biing-Hwang Juang,et al.  Speech recognition in adverse environments , 1991 .

[25]  Biing-Hwang Juang,et al.  The short-time modified coherence representation and noisy speech recognition , 1989, IEEE Trans. Acoust. Speech Signal Process..

[26]  Hynek Hermansky,et al.  RASTA-PLP speech analysis technique , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[27]  Biing-Hwang Juang,et al.  A family of distortion measures based upon projection operation for robust speech recognition , 1989, IEEE Trans. Acoust. Speech Signal Process..

[28]  Oded Ghitza,et al.  Auditory nerve representation as a front-end for speech recognition in a noisy environment , 1986 .