Speech recognition by concatenating fenonic allophone hidden Mar

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G10L 900

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055027911

ABSTRACT:
Analysis of a word input from a speech input device 1 for its features is made by a feature extractor 4 to obtain a feature vector sequence corresponding to said word, or to obtain a label sequence by applying a further transformation in a labeler 8. Fenonic hidden Markov models for speech transformation candidates are combined with N-gram probabilities (where N is all integer greater than or equal to 2) to produce models of words. The recognizer determines the probability that the speech model composed for each candidate word would output the label sequence or feature vector sequence input as speech, and outputs the candidate word corresponding to the speech model having the highest probability to a display 19.

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Bahl, L. R., et al. "Acoustic Markov Models Used In The Tangora Speech Recognition System." Proceedings of the 1988 IEEE International Conference on Acoustics, Speech, and Signal Processing, S11-13, pp. 497-500, Apr. 1988.
Bahl, L. R., et al. "A Maximum Likelihood Approach to Continuous Speech Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-5, No. 2, pp. 179-190, Mar. 1983.
Schwartz, R., et al. "Context-Dependent Modeling For Acoustic-Phonetic Recognition Of Continuous Speech." Proceedings of the 1985 IEEE International Conference on Acoustics, Speech, and Signal Processing, Tampa, Florida, pp. 1205-1208, Mar. 1985.

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