Author: Horacio Franco

In this paper we discuss a Gender Dependent Neural Network (GDNN) which can be tuned for each gender, while sharing most of the speaker independent parameters. We use a classification network to help generate gender-dependent phonetic probabilities for a statistical (HMM) recognition system.

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.

In this paper we present a new MLP architecture and training procedure for modeling context-dependent phonetic classes with a sequence of distributions.

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.

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.

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.