Accession Number : ADA326090
Title : Modular Neural Networks for Speech Recognition.
Descriptive Note : Master's thesis,
Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
Personal Author(s) : Fritsch, Juergen
PDF Url : ADA326090
Report Date : AUG 1996
Pagination or Media Count : 109
Abstract : In recent years, researchers have established the viability of so called hybrid NN/IIMM large vocabulary, speaker independent continuous speech recognition systems, where neural networks (NN) are used for the estimation of acoustic emission probabilities for hidden Markov models (IIMM) which provide statistical temporal modeling. Work in this direction is based on a proof, that neural networks can be trained to estimate posterior class probabilities. Advantages of the hybrid approach over traditional mixture of Gaussians based systems include discriminative training, fewer parameters, contextual inputs and faster sentence decoding. However, hybrid systems usually have training times that are orders of magnitude higher than those observed in traditional systems. This is largely due to the costly, gradient-based error-back propagation learning algorithm applied to very large neural networks, which often requires the use of specialized parallel hardware. This thesis examines how a hybrid NN/IIMM system can benefit from the use of modular and hierarchical neural networks such as the hierarchical mixtures of experts (IIME) architecture. Based on a powerful statistical framework, it is shown that modularity and the principle of divide-and-conquer applied to neural network learning reduces training times significantly. We developed a hybrid speech recognition system based on modular neural networks and the state-of-the-art continuous density IIMM speech recognizer JANUS. The system is evaluated on the English Spontaneous Scheduling Task (ESST), a 2400 word spontaneous speech database.
Descriptors : *NEURAL NETS, *SPEECH RECOGNITION, DATA BASES, MATHEMATICAL MODELS, ALGORITHMS, ADAPTIVE CONTROL SYSTEMS, STATE OF THE ART, MAXIMUM LIKELIHOOD ESTIMATION, LEARNING MACHINES, THESES, NONLINEAR SYSTEMS, LEAST SQUARES METHOD, HYBRID SYSTEMS, EXPERT SYSTEMS, SPEECH ANALYSIS, HIERARCHIES, MARKOV PROCESSES, ENGLISH LANGUAGE.
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE