A Posteriori Estimation of Correlated Jointly Gaussian Mean Vectors

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn PAMI-6, Issue 4, 1984, Pages 530-535

  Lasry, Moshé J ^a   Stern, Richard M ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Bayesian learning; dynamic speaker adaptation; maximum a posteriori probability; multivariate normal probability densities; speech recognition; statistical parameter estimation; statistical pattern recognition

Indexed keywords

PROBABILITY; SYSTEMS SCIENCE AND CYBERNETICS - LEARNING SYSTEMS;

BAYESIAN LEARNING; MAXIMUM A POSTERIORI PROBABILITY; PATTERN CLASSIFICATION PROBLEMS;

PATTERN RECOGNITION;

EID: 0021458298     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.1984.4767559     Document Type: Article

References (6)

1. R. A. Cole, R. M. Stern, M. S. Philips, S. M. Brill, A. P. Pilant, and P. Specker, “Feature-based speaker-independent recognition of isolated English letters,” in Proc. IEEE Int. Conf Acoust., Speech, Signal Processing, Boston, MA, 1983, pp. 731–733.
2. H. Cramer, Mathematical Methods of Statistics. Princeton, NJ: Princeton Univ. Press, 1946.
3. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
4. D. G. Keehn, “A note on learning for Gaussian processes,” IEEE Trans. Inform. Theory, vol. IT-11, pp. 126-132,1965.
5. R. M. Stern and M. J. Lasry, “Dynamic spenker adaptation for isolated letter recognition using MAP estimation,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Boston, MA, 1983, pp. 734–737.
6. H. L. Van Trees, Dectection, Estimation, and Modulation Theory, Part I. New York: Wiley, 1973, pp. 60–85.