Author: Victor Abrash
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Hybrid Neural Network/Hidden Markov Model Continuous Speech Recognition
In this paper we present a hybrid multilayer perceptron (MLP)/hidden Markov model (HMM) speaker-independent continuous-speech recognition system, in which the advantages of both approaches are combined by using MLPs to estimate the state-dependent observation probabilities of an HMM.
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Integrating Neural Networks into Computer Speech Recognition Systems
The work described here involved integrating neural networks into a hidden Markov model-based state-of-the-art continuous-speech recognition system, resulting in improvements in recognition accuracy and reductions in model complexity.
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Combining Neural Networks and Hidden Markov Models for Continuous Speech Recognition
We present a speaker-independent, continuous-speech recognition system based on a hybrid multilayer perceptron (MLP)/hidden Markov model (HMM). The system combines the advantages of both approaches by using MLPs to estimate the state-dependent observation probabilities of an HMM.
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Multiple-State Context-Dependent Phonetic Modeling with MLPs
In this paper we present a new MLP architecture and training procedure for modeling context-dependent phonetic classes with a sequence of distributions.
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Context-Dependent Connectionist Probability Estimation in a Hybrid HMM-Neural Net Speech Recognition System
In this paper we present a training method and a network architecture for the estimation of context-dependent observation probabilities in the framework of a hybrid Hidden Markov Model (HMM) / Multi Layer Perceptron (MLP) speaker independent continuous speech recognition system.