Author name cluster

Victor Abrash

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

NeurIPS Conference 1992 Conference Paper

Context-Dependent Multiple Distribution Phonetic Modeling with MLPs

Michael Cohen
Horacio Franco
Nelson Morgan
David Rumelhart
Victor Abrash

A number of hybrid multilayer perceptron (MLP)/hidden Markov model (HMM: ) speech recognition systems have been developed in recent years (Morgan and Bourlard. 1990). In this paper. we present a new MLP architecture and training algorithm which allows the modeling of context-dependent phonetic classes in a hybrid MLP/HMM: framework. The new training procedure smooths MLPs trained at different degrees of context dependence in order to obtain a robust estimate of the cootext-dependent probabilities. Tests with the DARPA Resomce Management database have shown substantial advantages of the context-dependent MLPs over earlier cootext(cid: 173) independent MLPs. and have shown substantial advantages of this hybrid approach over a pure HMM approach.

PDF Details

NeurIPS Conference 1992 Conference Paper

Modeling Consistency in a Speaker Independent Continuous Speech Recognition System

Yochai Konig
Nelson Morgan
Chuck Wooters
Victor Abrash
Michael Cohen
Horacio Franco

We would like to incorporate speaker-dependent consistencies, such as gender, in an otherwise speaker-independent speech recognition system. 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) recogni(cid: 173) tion system. The gender classification net predicts the gender with high accuracy, 98. 3% on a Resource Management test set. However, the in(cid: 173) tegration of the GDNN into our hybrid HMM-neural network recognizer provided an improvement in the recognition score that is not statistically significant on a Resource Management test set.

PDF Details