Title: A discriminative training algorithm for predictive neural network models
Abstract:Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. Those models, however, suffer from poor discrimination between acoustically similar spe...Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. Those models, however, suffer from poor discrimination between acoustically similar speech signals. In this paper, we propose a new discriminative training algorithm for predictive neural network models based on the generalized probabilistic descent (GPD) algorithm and the minimum classification error formulation. The proposed algorithm allows direct minimization of a recognition error rate. Evaluation of our training algorithm on Korean digits shows its effectiveness by 30% reduction of recognition error.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>Read More
Publication Year: 2002
Publication Date: 2002-12-17
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 2
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