A Bayesian classification approach with application to speech recognition

A Bayesian approach to classification of parametric information sources whose statistics are not explicitly given is studied and applied to recognition of speech signals based upon Markov modeling. A classifier based on generalized likelihood ratios, which depends only on the available training and testing data, is developed and shown to be optimal in the sense of achieving the highest asymptotic exponential rate of decay of the error probability. The proposed approach is compared to the standard classification approach used in speech recognition, in which the parameters for the sources are first estimated from the given training data, and then the maximum a posteriori decision rule is applied using the estimated statistics. >

[1]  Jacob Ziv,et al.  On classification with empirically observed statistics and universal data compression , 1988, IEEE Trans. Inf. Theory.

[2]  G. David Forney,et al.  Exponential error bounds for erasure, list, and decision feedback schemes , 1968, IEEE Trans. Inf. Theory.

[3]  Lalit R. Bahl,et al.  Maximum mutual information estimation of hidden Markov model parameters for speech recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  R. Gallager.  Information Theory and Reliable Communication , 1968 .

[5]  John E. Markel,et al.  Linear Prediction of Speech , 1976, Communication and Cybernetics.

[6]  Jacob Ziv,et al.  Universal decoding for finite-state channels , 1985, IEEE Trans. Inf. Theory.

[7]  Lawrence R. Rabiner,et al.  On the relations between modeling approaches for speech recognition , 1990, IEEE Trans. Inf. Theory.

[8]  S. Qureshi,et al.  Adaptive equalization , 1982, Proceedings of the IEEE.

[9]  Donald B. Rubin,et al.  Max-imum Likelihood from Incomplete Data , 1972 .

[10]  R. Gray,et al.  Distortion measures for speech processing , 1980 .

[11]  Peter F. Brown,et al.  The acoustic-modeling problem in automatic speech recognition , 1987 .

[12]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[13]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[14]  Arthur Nadas,et al.  Optimal solution of a training problem in speech recognition , 1985, IEEE Trans. Acoust. Speech Signal Process..

[15]  B. Atal.  Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. , 1974, The Journal of the Acoustical Society of America.

[16]  Michael Gutman,et al.  Asymptotically optimal classification for multiple tests with empirically observed statistics , 1989, IEEE Trans. Inf. Theory.

[17]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[18]  R. Gray,et al.  Speech coding based upon vector quantization , 1980, ICASSP.

[19]  S. Natarajan.  Large deviations, hypotheses testing, and source coding for finite Markov chains , 1985, IEEE Trans. Inf. Theory.

[20]  Yariv Ephraim.  Gain-adapted hidden Markov models for recognition of clean and noisy speech , 1992, IEEE Trans. Signal Process..

[21]  A. Barron.  THE STRONG ERGODIC THEOREM FOR DENSITIES: GENERALIZED SHANNON-MCMILLAN-BREIMAN THEOREM' , 1985 .

[22]  Biing-Hwang Juang,et al.  The segmental K-means algorithm for estimating parameters of hidden Markov models , 1990, IEEE Trans. Acoust. Speech Signal Process..

[23]  Mohamad Sawan,et al.  Speech Coding Based On Vector Quantization For A Tactile Vocoder Hearing Device , 1990, [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Lawrence R. Rabiner,et al.  A tutorial on Hidden Markov Models , 1986 .

[25]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[26]  Louis A. Liporace,et al.  Maximum likelihood estimation for multivariate observations of Markov sources , 1982, IEEE Trans. Inf. Theory.

[27]  Robert M. Gray,et al.  Rate-distortion speech coding with a minimum discrimination information distortion measure , 1981, IEEE Trans. Inf. Theory.

[28]  Neri Merhav,et al.  Maximum likelihood hidden Markov modeling using a dominant sequence of states , 1991, IEEE Trans. Signal Process..

[29]  F. Itakura,et al.  A statistical method for estimation of speech spectral density and formant frequencies , 1970 .

[30]  K. Dzhaparidze.  Parameter estimation and hypothesis testing in spectral analysis of stationary time series , 1986 .

[31]  Byoung-Seon Choi,et al.  Conditional limit theorems under Markov conditioning , 1987, IEEE Trans. Inf. Theory.

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

[33]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[34]  Lawrence R. Rabiner,et al.  A minimum discrimination information approach for hidden Markov modeling , 1989, IEEE Trans. Inf. Theory.

[35]  Roberto Billi,et al.  Vector quantization and Markov source models applied to speech recognition , 1982, ICASSP.