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A Maximum Likelihood Approach to Continuous Speech Recognition
February 1983 (vol. 5 no. 2)
pp. 179-190
| ASCII Text | x | ||
| Lalit R. Bahl, Frederick Jelinek, Robert L. Mercer, "A Maximum Likelihood Approach to Continuous Speech Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 2, pp. 179-190, February, 1983. | |||
| BibTex | x | ||
| @article{ 10.1109/TPAMI.1983.4767370, author = {Lalit R. Bahl and Frederick Jelinek and Robert L. Mercer}, title = {A Maximum Likelihood Approach to Continuous Speech Recognition}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {5}, number = {2}, issn = {0162-8828}, year = {1983}, pages = {179-190}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.1983.4767370}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - A Maximum Likelihood Approach to Continuous Speech Recognition IS - 2 SN - 0162-8828 SP179 EP190 EPD - 179-190 A1 - Lalit R. Bahl, A1 - Frederick Jelinek, A1 - Robert L. Mercer, PY - 1983 VL - 5 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the parameters for such models from sparse data. We also describe two decoding methods, one appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks. To illustrate the usefulness of the methods described, we review a number of decoding results that have been obtained with them.
Citation:
Lalit R. Bahl, Frederick Jelinek, Robert L. Mercer, "A Maximum Likelihood Approach to Continuous Speech Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 2, pp. 179-190, Feb. 1983, doi:10.1109/TPAMI.1983.4767370
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