Statistical-Model-Based Speech Enhancement Systems

Proceedings of the IEEE

Volumn 80, Issue 10, 1992, Pages 1526-1555

  Ephraim, Yariv ^a,b  

^a LUCENT TECHNOLOGIES   (United States)

^b GEORGE MASON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84948598244     PISSN: 00189219     EISSN: 15582256     Source Type: Journal    
DOI: 10.1109/5.168664     Document Type: Article

References (178)

1. J. S. Lim, Ed., Speech Enhancement. Englewood Cliffs, NJ: Prentice-Hall, 1983.
2. J. Makhoul et al, Removal of Noise from Noise-Degraded Speech Signals. Panel on removal of noise from a speech/noise signal, National Research Council. Washington, DC: National Academy Press, 1989.
3. J. S. Lim and A. V. Oppenheim, “Enhancement and bandwidth compression of noisy speech,” Proc. IEEE, Vol. 67, pp. 1586-1604, Dec. 1979.
4. S. F. Boll, “Speech enhancement in the 1980's: Noise suppression with pattern matching,” in Advances in Speech Signal Processing, S. Furui and M. M. Sondhi, Eds. New York: Marcel Dekker, 1992.
5. D. O'Shaughnessy, “Enhancing speech degraded by additive noise or interfering speakers,” IEEE Commun. Mag., pp. 46-52, Feb. 1989.
6. I. Pollack, “Speech communications at high noise levels: The role of a noise operated automatic gain control system and hearing protection,” J. Acoust. Soc. Amer., Vol. 29, pp. 1324-1327, Dec. 1957.
7. R. J. Niederjohn and J. H. Grotelueschen, “The enhancement of speech intelligibility in high noise levels by high-pass filtering followed by rapid amplitude compression,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 24, pp. 202-207, Aug. 1976.
8. R. J. Niederjohn and J. H. Grotelueschen, “Speech intelligibility enhancement in a power generating noise environment,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 26, pp. 208-210, Aug. 1978.
9. I. B. Thomas and R. J. Niederjohn, “The intelligibility of filtered-clipped speech in noise,” J. Audio Eng. Soc, Vol. 18, pp. 299-303, June 1970.
10. I. B. Thomas and W. J. Ohley, “Intelligibility enhancement through spectral weighting,” in Proc. IEEE Conf. Speech, Commun., and Processing, 1972, pp. 360-363.
11. J. C. R. Licklider, “Effects of amplitude distortion upon the intelligibility of speech,” J. Acoust. Soc. Amer., Vol. 29, pp. 429-434, Oct. 1946.
12. K. D. Kryter, J. C. R. Licklider, and S. S. Stevens, “Premodulation clipping in AM voice communication,” J. Acoust. Soc. Amer., Vol. 19, pp. 125-131, Jan. 1947.
13. E. A. Kretsinger and N. B. Young, “The use of fast limiting to improve the intelligibility of speech in noise,” Speech Monographs; Vol. 27, No. 1, Mar. 1960.
14. J. S. Lim and A. V. Oppenheim, “Reduction of quantization noise in PCM speech coding,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 28, pp. 107-110, Feb. 1980.
15. Y. Ephraim and D. Malah, “Combined enhancement and adaptive transform coding of noisy speech,” Proc. Inst. Elec. Eng., Vol. 133, pt. F, no. 1, pp. 81-86, Feb. 1986.
16. I. B. Thomas and A. Ravindran, “Intelligibility enhancement of already noisy speech signals,” J. Audio Eng. Soc., Vol. 22, pp. 234-236, May 1974.
17. S. F. Boll, “Suppression of acoustic noise in speech using spectral subtraction,” IEEE Trans. Acoust., Speech, Signal Process. Vol. 27, pp. 113-120, Apr. 1979.
18. B. Widrow et al, “Adaptive noise cancellation: Principles and applications,” Proc. IEEE, Vol. 63, pp. 1692-1716, Dec. 1975.
19. J. S. Lim and A. V. Oppenheim, “All-pole modeling of degraded speech,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 26, pp. 197-210, June 1978.
20. M. R. Weiss, E. Aschkenasy, and T. W. Parsons, “Processing speech signals to attenuate interference,” in IEEE Symp. Speech Recognition (Pittsburgh, PA), Apr. 1974 pp. 292-293.
21. A. Feit, “Intelligibility enhancement of noisy speech signals,” M.Sc. thesis, Department of Electrical Engineering, Technion-Israel Institute of Technology, July 1973 (in Hebrew).
22. M. Berouti, R. Schwartz, and J. Makhoul, “Enhancement of speech corrupted by acoustic noise,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Aprl 1979, pp. 208-211.
23. J. S. Lim, “Evaluation of correlation subtraction method for enhancing speech degraded by additive white noise,” IEEE Trans. Acoust, Speech, Signal Process., Vol. 26, pp. 471-472, Oct. 1978.
24. R. J. McAulay and M. L. Malpass, “Speech enhancement using a soft-decision noise suppression filter,” IEEE Trans. Acoust, Speech, Signal Process., Vol. 28, pp. 137-145, Apr. 1980
25. M. M. Sondhi, C. E. Schmidt, and L. R. Rabiner, “Improving the quality of a noisy speech signal,” Bell Syst. Tech. J., Vol. 60, No. 8, pp. 1847-1859, Oct. 1981.
26. P. Vary, “On the enhancement of noisy speech,” in Signal Processing IT. Theories and Applications. New York: Elsevier, 1983, pp. 327-330.
27. D. L. Wang and J. S. Lim, “The unimportance of phase in speech enhancement,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 30, pp. 679-681, Aug. 1982.
28. Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error short time spectral amplitude estimator,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 32, pp. 1109-1121, Dec. 1984;
29. Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Boston), Apr. 1983 pp. 1118-1121.
30. Y. Ephraim, “A Bayesian estimation approach for speech enhancement using hidden Markov models,” IEEE Trans. Signal Process., Vol. 40, pp. 725-735, Apr. 1992.
31. Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error log-spectral amplitude estimator,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 33, pp. 443-445, Apr. 1985.
32. H. Drucker, “Speech processing in a high ambient noise environment,” IEEE Trans. Audio Electroacoust., Vol. AU-16, No. 2, pp. 165-168, June 1968.
33. J. H. L. Hansen and M. A. Clements, “Iterative speech enhancement with spectral constraints,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1987, pp. 189-192.
34. K. K. Paliwal, “Speech enhancement using multipulse excited linear prediction filter,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1986, pp. 101-104.
35. K. K. Paliwal, “A speech enhancement method based on Kalman filtering,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1987, pp. 177-180.
36. B. Koo, J. D. Gibson, and S. D. Gray, “Filtering of colored noise for speech enhancement and coding,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, May 1989, pp. 349-352.
37. J. E. Porter and S. F. Boll, “Optimal estimators for spectral restoration of noisy speech,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Mar. 1984, pp. 18A.2.1-18A.2.4.
38. Y. Ephraim, D. Malah, and B.-H. Juang “On the application of hidden Markov models for enhancing noisy speech,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 37, pp. 1846-1856, Dec. 1989.
39. D. O'Shaughnessy, “Speech enhancement using vector quantization and a formant distance measure,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1988, pp. 549-552.
40. T. F. Quatieri and R. J. McAulay, “Noise reduction using a soft-decision sine-wave vector quantizer,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1990, pp. 821-824.
41. Y. Ephraim, “A minimum mean square error approach for speech enhancement,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Albuquerque, NM), Apr. 1990 pp. 829-832.
42. Y. Ephraim and N. Merhav, “Lower and upper bounds on the minimum mean square error in composite source signal estimation,” IEEE Trans. Inform. Theory, Vol. 38, Nov. 1992.
43. S. Tamura and A. Waibel, “Noise reduction using connectionist models,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1988, pp. 553-556.
44. S. Tamura and M. Nakamura, “Improvement to the noise reduction neural network,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1990, pp. 825-828.
45. M. W. White, R. M. Holdaway, J. J. Paulos, and S. Tambwekar, “The goal of perceptual fidelity in speech enhancement,” Tech. Rep. NETR-89-7, North Carolina State University.
46. M. S. Ahmed, “Comparison of noisy speech enhancement algorithms in terms of LPC perturbation,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 37, pp. 121-125, Jan. 1989.
47. K. K. Paliwal, “Estimation of noise variance from the noisy AR signal and its application in speech enhancement,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 36, pp. 292-294, Feb. 1988.
48. A-Chuan Hsueh and C. K. Chuang, “A multipulse excited pole-zero filtering approach for speech enhancement,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1988, pp. 545-548.
49. M. R. Sambur, “Adaptive noise canceling for speech signals,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 26, pp. 419-423, Oct. 1978.
50. J. W. Kim and C. K. Un, “Enhancement of noisy speech by forward/backward adaptive digital filtering,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1986, pp. 89-92.
51. Y. Ariki, K. Kajimoto, and T. Sakai, “Acoustic noise reduction by two dimensional spectral smoothing and spectral amplitude transformation,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1986, pp. 97-100.
52. D. E. Veeneman and B. Mazor, “A fully adaptive comb filter for enhancing block-coded speech,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 37, pp. 995-957, June 1989.
53. T. W. Parsons, “Separation of speech from interfering speech by means of harmonic selection,” J. Acoust. Soc. Amer., Vol. 60, pp. 911-918, Oct. 1976.
54. N. D. Degan and C. Prati, “Performance of speech enhancement techniques for mobile radio terminal applications,” in Signal Processing III: Theories and Applications. New York: Elsevier Publishers B.V. (North Holland), 1986, pp. 381-385.
55. G. S. Kang and L. J. Fransen, “Quality improvement of LPC processed noisy speech by using spectral subtraction,” IEEE Trans. Acoust. Speech, Signal Process., Vol. 37, pp. 939-942, June 1989.
56. T. Berger, Rate Distortion Theory: A Mathematical Basis for Data Compression. Englewood Cliffs, NJ: Prentice-Hall, 1971.
57. D. B. Paul, “The spectral envelope estimation vocoder,” IEEE Trans. Acoust. Speech, Signal Process., Vol. 29, pp. 786-794, Aug. 1981.
58. J. D. Gibson, T. R. Fisher, and B. Koo, “Estimation and vector quantization of noisy speech,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, Apr. 1988, pp. 541-544.
59. R. L. Dobrushin and B. S. Tsybakov, “Information transmission with additional noise,” IRE Trans. Inform. Theory, vol. 18, pp. S293–S304, 1962.
60. T. Fine, “Optimum mean-square quantization of a noisy input,” IEEE Trans. Inform. Theory, pp. 293-294, Apr. 1965.
61. D. J. Sakrison, “Source encoding in the presence of random disturbance,” IEEE Trans. Inform. Theory, pp. 165-167, Jan. 1968.
62. H. S. Witsenhausen, “Indirect rate-distortion problems,” IEEE Trans. Inform. Theory, Vol. IT-26, pp. 518-521, Sept. 1980.
63. Y. Ephraim and R. M. Gray, “A unified approach for encoding clean and noisy sources by means of waveform and autoregressive model vector quantization,” IEEE Trans. Inform. Theory, Vol. 34, pp. 826-834, July 1988.
64. B.-H. Juang and L. R. Rabiner, “Signal restoration by spectral mapping,” Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, Apr. 1987, pp. 2368-2371.
65. J. K. Wolf and J. Ziv, “Transmission of noisy information to a noisy receiver with minimum distortion,” IEEE Trans. Inform. Theory, Vol. 16, pp. 406-411, July 1970.
66. E. Ayanoglu, “On optimal quantization of noisy sources,” IEEE Trans. Inform. Theory, Vol. IT-36, pp. 1450-1452, Nov. 1990.
67. M. R. Sambur, “A preprocessing filter for enhancing LPC analysis/synthesis of noisy speech,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, Apr. 1979, pp. 971-974.
68. J. L. Melsa and J. D. Tomcik, “Linear predictive coding with additive noise for application to speech digitization,” in Proc. 14th Allerton Conf. Circuit and Systems Theory, 1976, pp. 500-508.
69. R. P. Preuss, “A frequency domain noise canceling preprocessor for narrowband speech communication systems,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, 1979, pp. 212-215.
70. B. R. Musicus and J. S. Lim, “Maximum likelihood parameter estimation of noisy data,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, 1979, pp. 224-227.
71. D. P. McGinn and D. H. Johnson, “Estimation of all-pole parameters from noise corrupted sequences,” in Proc. ASSP Spectrum Estimation Workshop II, 1983, pp. 108-111.
72. S. Kay, “Improvement of autoregressive spectral estimates in the presence of noise,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, 1978, pp. 357-360.
73. L. Marple, “High resolution autoregressive spectrum analysis using noise power cancellation,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, 1978, pp. 345-348.
74. S. M. Kay and S. L. Marple, Jr., “Spectrum analysis—A modern perspective,” Proc. IEEE, Vol. 69, pp. 1380-1419, Nov. 1981.
75. V. K. Jain and B. S. Atal, “Robust LPC analysis of speech by extended correlation matching,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, 1985, pp. 473-476.
76. R. D. Preuss and R. Yarlagadda, “Autoregressive spectral estimation in noise in the context of speech analysis,” in Proc. ASSP Spectrum Estimation Workshop II (Tempa, FL), Nov. 1983 pp. 75-79.
77. W. J. Done and C. K. Rushforth, “Estimating the parameters of a noisy all-pole process using pole-zero modeling,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, 1979, pp. 228-231.
78. M. Pagano, “Estimation of models of autoregressive signal plus white noise,” Ann. Statist., vol. 2, pp. 99–108, 1974.
79. S. M. Kay, “Noise compensation for autoregressive spectral estimates,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 28, pp. 292-303, June 1980.
80. Y. Ephraim, “On minimum mean square error speech enhancement,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing (Toronto), May 1991 pp. 997-1000
81. A. P. Varga and R. K. Moore, “Hidden Markov model decomposition of speech and noise,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1990, pp. 845-848.
82. A. Nádas, D. Nahamoo, and M. A. Picheny, “Speech recog nition using noise-adaptive prototypes,” IEEE Trans. Acoust. Speech, Signal Process., Vol. 37, pp. 1495-1503, Oct. 1989.
83. Y. Ephraim, “Gain-adapted hidden Markov models for recognition of clean and noisy speech,” IEEE Trans. Signal Processing,, Vol. 40, pp. 1303-1316, June 1992.
84. N. Merhav and Y. Ephraim, “A Bayesian classification approach with application to speech recognition,” IEEE Trans. Signal Processing, Vol. 39, pp. 2157-2166, Oct. 1991.
85. T. Kailath, “A general likelihood-ratio formula for random signals in Gaussian noise,” IEEE Trans. Inform. Theory, Vol. 15, pp. 350-361, May 1969.
86. A. Acero and R. M. Stern, “Environmental robustness in automatic speech recognition,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1990, pp. 849-852.
87. A. Erell and M. Weintraub, “Estimation using log-spectral distance criterion for noise robust speech recognition,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1990, pp. 853-855.
88. H. Gish, Y.-L. Chow, and R. Rohlicek, “Probabilistic vector mapping of noisy speech parameters for HMM word spotting,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, Apr. 1990, pp. 117-120.
89. G. A. Neben, R. J. McAulay, and C. J. Weinstein, “Experiments in isolated word recognition using noisy speech,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1983, pp. 1156-1159.
90. D. V. Compernolle, “Spectral estimation using a log-distance error criterion applied to speech recognition,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, May 1989, pp. 258-261.
91. D. Van Compernolle, “Noise adaptation in a hidden Markov model speech recognition system,” Computer Speech and Language, Vol. 3, No. 2, pp. 151-167, Apr. 1989.
92. J. H. L. Hansen and M. A. Clements, “Constrained iterative speech enhancement with application to speech recognition,” IEEE Trans. Acoust. Speech, Signal Process., Vol. 39, pp. 795-805, Apr. 1991.
93. J. H. L. Hansen and M. A. Clements, “Stress compensation and noise reduction algorithms for robust speech recognition,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, May 1989, pp. 266-269.
94. D. Mansour and B.-H. Juang, “The short-time modified coherence representation and noisy speech recognition,” IEEE Trans. Acoust. Speech, Signal Process., Vol. 37, pp. 795-804, June 1989.
95. I. Lecomte, M. Lever, J. Boudy, and A. Tassy, “Car noise processing for speech input,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, May 1989, pp. 512-515.
96. A. Noll et al., “Real-time connected-word recognition in a noisy environment,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, May 1989, pp. 679-682.
97. B. P. Landell, R. E. Wohlford, and L. G. Bahler, “Improved speech recognition in noise,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Tokyo), 1986, pp. 14.14.1-14.14.3.
98. J. N. Holmes and N. C. Sedgewick, “Noise compensation for speech recognition using probabilistic models,” in Proc. IEEE Int. Conf. Acoust, Speech, Signal Processing, Apr. 1986, pp. 741-744.
99. D. B. Roe, “Speech recognition with a noise-adapting code-book,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, Apr. 1987, pp. 1139-1142.
100. A. Varga, R. Moore, J. Bridle, K. Ponting, and M. Russell, “Noise compensation algorithms for use with hidden Markov model based speech recognition,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1988, pp. 481-484.
101. O. Ghitza, “Robustness against noise: the role of timing-synchrony measurements,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, Apr. 1987, pp. 2372-2375.
102. M. J. Hunt and C. Lefebvre, “A comparison of several acoustic representations for speech recognition with degraded and un-degraded speech,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing (Glasgow), May 1989 pp. 262-265.
103. H. Matsumoto and H. Imai, “Comparative study of various spectrum matching measures on noise robustness,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1986, pp. 769-772.
104. B. A. Hanson and H. Wakita, “Spectral slope distance measures with linear prediction analysis for word recognition in noise,” IEEE Trans. Acoust. Speech, Signal Process., Vol. 35, pp. 968-973, July 1987.
105. F. Soong and M. M. Sondhi, “A frequency-weighted Itakura spectral distortion measure and its application to speech recognition in noise,” IEEE Trans. Acoust. Speech, Signal Process., Vol. 36, pp. 41-48, Jan. 1988.
106. J.-C. Junqua and H. Wakita, “A comparative study of cepstral lifters and distance measures for all pole models of speech in noise,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing (Glasgow), May 1989 pp. 476-479.
107. D. Mansour and B.-H. Juang, “A family of distortion measures based upon projection operation for robust speech recognition,” IEEE Trans. Acoust. Speech, Signal Process., Vol. 37, pp. 1659-1671, Nov. 1989.
108. N. Merhav and C.-H. Lee, “A minimax classification approach with application to robust speech recognition,” IEEE Trans. Speech and Audio Processing, to be published.
109. J. D. Ferguson, Ed., Proc. Symp. Applications of Hidden Markov Models to Text and Speech. IDA-CRD, Princeton, NJ, 1980.
110. A. B. Poritz, “Hidden Markov models: A guided tour,” in Proc. IEEE Int. Conf. on Acoust., Speech, Signal Processing, Apr. 1988, pp. 7-13.
111. L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proc. IEEE, pp. 257-286, Feb. 1989.
112. C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., Vol. 27, pp. 379-423, 623-656, 1948.
113. R. G. Gallager, Information Theory and Reliable Communication. New York: Wiley, 1968.
114. L. E. Baum and T. Petrie, “Statistical inference for probabilistic functions of finite state Markov chains,” Ann. Math. Statist., vol. 37, pp. 1554–1563, 1966.
115. L. E. Baum, T. Petrie, G. Soules, and N. Weiss, “A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains,” Ann. Math. Stat., vol. 41, no. 1, pp. 164-171, 1970.
116. L. E. Baum, “An inequality and associated maximization technique in statistical estimation of probabilistic functions of Markov processes,” Inequalities, vol. 3, no. 1, pp. 1-8, 1972.
117. L. A. Liporace, “Maximum likelihood estimation for multivariate observations of Markov sources,” IEEE Trans. Inform. Theory, Vol. 28, pp. 729-734, Sept. 1982.
118. S. E. Levinson, L. R. Rabiner, and M. M. Sondhi, “An introduction to the application of the theory of probabilistic functions of Markov process to automatic speech recognition,” Bell Syst. Tech. J., Vol. 62, No. 4, pp. 1035-1074, Apr. 1983.
119. C.-H. Lee, L. R. Rabiner, R. Pieraccini, and J. G. Wilpon, “Acoustic modeling for large vocabulary,” Comput. Speech Language, vol. 4, pp. 127–165, 1990.
120. C. J. Wellekens, “Explicit time correlation in hidden Markov models for speech recognition,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1987, pp. 384-386.
121. Y. Ephraim, “Speech enhancement using state dependent dynamical system model,” in Proc. IEEE Int. Conf. Acoust, Speech, Signal Processing, Mar. 1992, pp. 289-292.
122. B.-H. Juang and L. R. Rabiner, “Mixture autoregressive hidden Markov models for speech signals,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 23, pp. 1404-1413, Dec. 1985.
123. N. Merhav and Y. Ephraim, “Maximum likelihood hidden Markov modeling using a dominant sequence of states,” IEEE Trans. Signal Processing, Vol. 39, pp. 2111-2115, Sept. 1991.
124. N. Merhav and Y. Ephraim, “Hidden Markov modeling using a dominant state sequence with application to speech recognition,” Comput. Speech Language, vol. 5, pp. 327–339, 1991.
125. A. B. Poritz, “Linear predictive hidden Markov models and the speech signal,” in Proc. of the Symposium on the applications of hidden Markov models to text and speech (Princeton, NJ), 1980, pp. 88-142;
126. Proc. IEEE Int. Conf. on Acoust, Speech, Signal Processing, Apr. 1982, pp. 1291-1294.
127. Y. Ephraim, A. Dembo, and L. R. Rabiner, “A minimum discrimination information approach for hidden Markov modeling,” IEEE Trans. Inform. Theory, Vol. 35, pp. 1001-1013, Sept. 1989.
128. Y. Ephraim and L. R. Rabiner, “On the relations between modeling approaches for speech recognition,” IEEE Trans. Inform. Theory, Vol. 36, pp. 372-380, Mar. 1990.
129. L. R. Rabiner, J. G. Wilpon, and B.-H. Juang, “A segmental κ-means training procedure for connected word recognition,” AT&T Tech. J., pp. 21-40, May-June 1986.
130. B.-H. Juang and L. R. Rabiner, “The segmental κ-means algorithm for estimating parameters of hidden Markov models,” IEEE Trans. Acoust, Speech, Signal Process., pp. 1639-1641, Sept. 1990. Distortion Measures
131. F. Itakura and S. Saito, “A statistical method for estimation of speech spectral density and format frequencies,” Electron. Commun. Japan, vol. 53-A, no. 1, pp. 36-43, 1970.
132. R. M. Gray, A. Buzo, A. H. Gray, Jr., and Y. Matsuyama, “Distortion measures for speech processing,” IEEE Trans. Acoust, Speech, Signal Process., Vol. ASSP-28, pp. 367-376, Aug. 1980; vol. 34, pp. 1033-1040, Sept. 1988.
133. K. Dzhaparidze, Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series. New York: Springer- Verlag, 1986.
134. Y. Ephraim, H. Lev-Ari, and R. M. Gray, “Asymptotic minimum discrimination information measure for asymptotically weakly stationary processes,” IEEE Trans. Inform. Theory, Vol. 34, pp. 1033-1040, Sept. 1988.
135. M. Basseville, “Distance measures for signal processing and pattern recognition,” Signal Processing, Vol. 18, No. 4, pp. 349-369, Dec. 1989.
136. A. Gersho and V. Cuperman, “Vector quantization: A pattern matching technique for speech coding,” IEEE Commun. Mag., Vol. 21, pp. 15-21, Dec. 1983.
137. R. M. Gray, “Vector quantization,” IEEE ASSP Mag., pp. 4-29, Apr. 1984.
138. J. Makhoul, S. Roucos, and H. Gish, “Vector quantization in speech coding,” Proc. IEEE, vol. 73, pp. 1551–1588, 1985.
139. A. Buzo, A. H. Gray, Jr., R. M. Gray, and J. D. Markel, “Speech coding based upon vector quantization,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 28, pp. 562-574, Oct. 1980.
140. R. M. Gray, A. H. Gray, Jr., G. Rebolledo, and J. E. Shore, “Rate-distortion speech coding with a minimum discrimination information distortion measure,” IEEE Trans. Inform. Theory, Vol. 27, pp. 708-721, Nov. 1981.
141. M. J. Sabin and R. M. Gray, “Product code vector quantizers for waveform and voice coding,” IEEE Trans. Acoust. Speech, Signal Process., Vol. 32, pp. 474-488, June 1984.
142. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Boston: Kluwer Academic 1991.
143. J. Foster, R. M. Gray, and M. O. Dunham, “Finite-state vector quantization for waveform coding,” IEEE Trans. Inform. Theory, Vol. 31, pp. 348-359, May 1985.
144. M. O. Dunham and R. M. Gray, “An algorithm for the design of labeled-transition finite-state vector quantizers,” IEEE Trans. Commun., vol. 33, pp. 83–89, 1985.
145. L. C. Stewart, R. M. Gray, and Y. Linde, “The design of trellis waveform coders,” IEEE Trans. Commun., Vol. pp. 702-710, Apr. 1982.
146. M. J. Sabin and R. M. Gray, “Global convergence and empirical consistency of the generalized Lloyd algorithm,” IEEE Trans. Inform. Theory, Vol. 32, pp. 148-155, Mar. 1986
147. R. M. Gray and E. Karnin, “Multiple local optima in vector quantizers,” IEEE Trans. Inform. Theory, Vol. 28, pp. 256-261, Mar. 1982.
148. R. M. Gray, J. C. Kieffer, and Y. Linde, “Locally optimal block quantizer design,” Information and Control, pp. 178-198, May 1980.
149. A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. Royal Stat. Soc. B, vol. 39, pp. 1–38, 1977.
150. C. F. J. Wu, “On the convergence properties of the EM algorithm,” Ann. Statist., Vol. 11, No. 1, pp. 95-103, 1983.
151. D. T. Magill, “Optimal adaptive estimation of sampled stochastic processes,” IEEE Trans. Automat. Contr., Vol. 10, pp. 434-439, Oct. 1965;
152. cf. author's reply, IEEE Trans. Automat. Contr., Vol. 14, pp. 216-218, Apr. 1969.
153. F. L. Sims and D. G. Lainiotis, “Recursive algorithm for calculation of the adaptive Kalman filter weighing coefficients,” IEEE Trans. Automat. Contr., Vol. 14, pp. 215-217, Apr. 1969.
154. D. G. Lainiotis, “Optimal adaptive estimation: Structure and parameter adaptation,” IEEE Trans. Automat. Contr., Vol. 16, pp. 160-170, Apr. 1971.
155. L. A. Liporace, “Variance of Bayes estimators,” IEEE Trans. Inform. Theory, Vol. 17, pp. 665-669, Nov. 1971.
156. D. Kazakos, “New convergence bounds for Bayes estimators,” IEEE Trans. Inform. Theory, Vol. 27, pp. 97-104, Jan. 1981.
157. L. Merakos and D. Kazakos, “Comments and corrections to new convergence bounds for Bayes estimators,” IEEE Trans. Inform. Theory, Vol. 29, pp. 318-320, Mar. 1983.
158. G. Saridis, Self Organizing Control of Stochastic System. New York: Marcel Dekker, 1977.
159. H. L. Van-Trees, Detection, Estimation and Modulation Theory, part I. New York: Wiley, 1968.
160. J. D. Markel and A. H. Gray, Jr., Linear Prediction of Speech. New York: Springer-Verlag, 1976.
161. L. R. Rabiner and R. W. Schafer, Digital Processing of Speech Signals. Englewood Cliffs, NJ: Prentice-Hall, 1978.
162. T. Kailath Lectures on Linear Least-Square Estimation. New York: Springer-Verlag, 1976.
163. R. M. Gray, Probability, Random Processes, and Ergodic Properties. New York: Springer-Verlag, 1988.
164. T. M. Cover, “A hierarchy of probability density function estimates,” in Frontiers of Pattern Recognition, S. Watanabe, Ed. New York: Academic Press, 1972.
165. R. A. Redner and H. F. Walker, “Mixture densities, maximum likelihood and the EM algorithm,” SIAM Review, Vol. 26, No. 2, pp. 195-239, Apr. 1984.
166. T. F. Quatieri and R. J. McAulay, “Phase coherence in speech reconstruction for enhancement and coding applications,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, May 1989, pp. 207-210.
167. I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products. New York: Academic, 1980.
168. R. M. Gray, “Toeplitz and circulant matrices: II,” Tech. Rep. 6504-1, Stanford Electron. Lab., Apr. 1977.
169. U. Grenander and G. Szego, Toeplitz Forms and Their Applications. New York: Chelsea Publishing, 1984.
170. L. R. Rabiner and S. E. Levinson, “Isolated and connected word recognition theory and selected applications,” IEEE Trans. Commun., Vol. 29, pp. 621-659, May 1981.
171. J. M. Tribolet and R. E. Crochiere, “Frequency domain coding of speech,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 27, pp. 512-530, Oct. 1979.
172. J. M. Tribolet and R. E. Crochiere, “A modified adaptive transform coding scheme with post-processing-enhancement,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Apr. 1980, pp. 336-339.
173. J. S. Lim, A. V. Oppenheim, and L. D. Braida “Evaluation of an adaptive comb filtering method for enhancing speech degraded by white noise addition,” IEEE Trans. Acoust., Speech, Signal Process., Vol. 26, pp. 354-358, Aug. 1978.
174. S. Haykin, Ed., Advances in Spectrum Analysis and Array Processing,, Vol. I. Englewood Cliffs, NJ: Prentice-Hall, 1991.
175. E. Levin, “Word recognition using hidden control neural ar chitecture,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Albuquerque, NM), May 1990 pp. 433-436.
176. C. L. Nikias and M. R. Raghuveer, “Bispectrum estimation: A digital signal processing framework,” Proc. IEEE, pp. 869-891, July 1987.
177. J. M. Mendel, “Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications,” Proc. IEEE, pp. 278-305, Mar. 1991.
178. G. D. Forney, Jr., “The Viterbi algorithm,” Proc. IEEE, Vol. 61, pp. 268-278, Mar. 1973.