A speech recognizer optimally combining learning vector quantization, dynamic programming and multi-layer perceptron

The authors give a detailed description of a new hybrid system for acoustic decoding. The system features cooperation between a multilayer perceptron (MLP) and an adaptive dynamic programming (DP) module. They show how to train the whole system in an optimal way using an adaptive gradient technique. The DP module optimizes cost functions inspired from k-means and learning vector quantization (LVQ). This module allows the training of synthetic references which incorporate discriminant information and improves the performance and/or speed of usual dynamic programming systems. The authors analyze and provide solutions to some problems which may occur when training the whole hybrid system and show that they are common to many modular architectures. These theoretical issues are illustrated through experiments on an isolated-word database