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Hybrid Neural Network/Hidden Markov Model Systems for Continuous Speech Recognition.

, , , , and . IJPRAI, 7 (4): 899-916 (1993)

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Generalization and Parameter Estimation in Feedforward Nets: Some Experiments, and . TR-89-017. International Computer Science Institute, Berkeley, CA, (1989)Long-Term Temporal Features for Conversational Speech Recognition., , and . MLMI, volume 3361 of Lecture Notes in Computer Science, page 232-242. Springer, (2004)Tonotopic Multi-Layered Perceptron: A Neural Network for Learning Long-Term Temporal Features for Speech Recognition., , and . ICASSP (1), page 945-948. IEEE, (2005)A neural network based, speaker independent, large vocabulary, continuous speech recognition system: the WERNICKE project., , , , , , , , , and 4 other author(s). EUROSPEECH, page 1941-1944. ISCA, (1993)Towards handling the acoustic environment in spoken language processing., and . ICSLP, page 85-88. ISCA, (1992)CDNN: a context dependent neural network for continuous speech recognition., , , and . ICASSP, page 349-352. IEEE Computer Society, (1992)Statistical Inference in Multilayer Perceptrons and Hidden Markov Models with Applications in Continuous Speech Recognition., , and . NATO Neurocomputing, volume 68 of NATO ASI Series, page 217-226. Springer, (1989)Connectionist Approaches to the Use of Markov Models for Speech Recognition., , and . NIPS, page 213-219. Morgan Kaufmann, (1990)Stochastic perceptual models of speech., , , , and . ICASSP, page 397-400. IEEE Computer Society, (1995)On using MLP features in LVCSR., , , and . INTERSPEECH, page 921-924. ISCA, (2004)