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A Continuous Speech Recognition System Embedding MLP into HMM
1989
Neural Information Processing Systems
We are developing a phoneme based. speaker-dependent continuous speech recognition system embedding a Multilayer Perceptron (MLP) (Le .• a feedforward Artificial Neural Network). into a Hidden Markov Model (HMM) approach. In [Bourlard & Wellekens]. it was shown that MLPs were approximating Maximum a Posteriori (MAP) probabilities and could thus be embedded as an emission probability estimator in HMMs. By using contextual information from a sliding window on the input frames. we have been able
dblp:conf/nips/BourlardM89
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