Introduction to the special issue on new computational paradigms for acoustic modeling in speech recognition

Computer Speech and Language

Volumn 17, Issue 2-3, 2003, Pages 107-112

  Russell, Martin J ^a   Bilmes, Jeff A ^b  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC NOISE; ACOUSTIC SIGNAL PROCESSING; COMPUTER SIMULATION; MARKOV PROCESSES; SPEECH ANALYSIS; SPEECH PROCESSING; VOCABULARY CONTROL;

ACOUSTIC MODELING; HIDDEN MARKOV MODEL; SPEECH PATTERN PROCESSING; SPEECH TRANSCRIPTION;

SPEECH RECOGNITION;

EID: 0038359577     PISSN: 08852308     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0885-2308(03)00013-5     Document Type: Editorial

References (10)

1. Allen, J.A., 1994. How do humans process and recognize speech? IEEE Trans. Speech Audio Process. 2 (4), 567-576.
2. Bilmes, J.A., 2003. Graphical models and automatic speech recognition. In: Rosenfeld, R., Ostendorf, M., Khudanpur, S., Johnson, M. (Eds.), Mathematical Foundations of Speech and Language Processing. Springer, New York.
3. Bourlard, H., 1995. Towards increasing speech recognition error rates. In: Proceedings of the ESCA European Conference on Speech Communication and Technology'95, Madrid, Spain, vol. 2, pp. 883-894.
4. Dean, T., Kanazawa, K., 1988. Probabilistic temporal reasoning. AAAI, 524-528.
5. Fletcher, H., 1953. Speech and Hearing in Communication. Krieger, New York.
6. Gharamani, Z., Jordan, M.I., 1997. Factorial hidden Markov models. Mach. Learning 29, 245-273.
7. Lauritzen, S., 1996. Graphical Models. Oxford Science Publications.
8. Ostendorf, M., Digalakis, V., Kimball, O.A., 1996. From HMMs to segment models: a unified view of stochastic models for speech recognition. IEEE Trans. SAP 4 (5), 360-378.
9. Pearl, J., 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo, CA.
10. Smyth, P., Heckerman, D., Jordan, M.I., 1996. Probabilistic independence networks for hidden Markov probability models. Technical Report A.I. Memo No. 1565, C.B.C.L. Memo No. 132, MIT AI Lab and CBCL.