Discriminative feature extraction for speech recognition

A novel approach to pattern recognition, called discriminative feature extraction (DFE) is introduced as a way to interactively handle the input data with a given classifier. The entire recognizer, consisting of the feature extractor as well as the classifier, is trained with the minimum classification error generalised probabilistic descent learning algorithm. Both the philosophy and implementation examples of this approach are described. DFE realizes a significant departure from conventional approaches, providing a comprehensive base for the entire system design. By way of example, an automatic scaling process is described, and experimental results for designing a cepstrum representation for vowel recognition are presented.<<ETX>>

[1]  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.

[2]  Shigeru Katagiri,et al.  Application of a generalized probabilistic descent method to dynamic time warping-based speech recognition , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Yoh'ichi Tohkura,et al.  A weighted cepstral distance measure for speech recognition , 1987, IEEE Trans. Acoust. Speech Signal Process..

[4]  Biing-Hwang Juang,et al.  New discriminative training algorithms based on the generalized probabilistic descent method , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.

[5]  Shigeru Katagiri,et al.  A generalized probabilistic descent method , 1990 .

[6]  Chin-Hui Lee,et al.  Segmental GPD training of HMM based speech recognizer , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Biing-Hwang Juang,et al.  Discriminative learning for minimum error classification [pattern recognition] , 1992, IEEE Trans. Signal Process..

[8]  E. Zwicker,et al.  Subdivision of the audible frequency range into critical bands , 1961 .

[9]  Shigeru Katagiri,et al.  Prototype-based discriminative training for various speech units , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Biing-Hwang Juang,et al.  On the use of bandpass liftering in speech recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.