Accession Number : ADA261523
Title : Supervised and Unsupervised Feature Extraction from a Cochlear Model for Speech Recognition
Descriptive Note : Technical rept.
Corporate Author : BROWN UNIV PROVIDENCE RI INST FOR BRAIN AND NEURAL SYSTEMS
Personal Author(s) : Intrator, N ; Tajchman, G
Report Date : 23 Dec 1992
Pagination or Media Count : 11
Abstract : We explore the application of a novel classification method that combines supervised and unsupervised training, and compare its performance to various more classical methods. We first construct a detailed high dimensional representation of the speech signal using Lyon's cochlear model and then optimally reproduce its dimensionality. The resulting low dimensional projection retains the information needed for robust speech recognition.
Descriptors : *MODELS , *SPEECH RECOGNITION , NEURAL NETS , TRAINING , PERFORMANCE(HUMAN) , SPEECH , MOTOR NEURONS , LEARNING , EXTRACTION , SIGNALS , CLASSIFICATION , RECOGNITION
Subject Categories : Anatomy and Physiology
Test Facilities, Equipment and Methods
Distribution Statement : APPROVED FOR PUBLIC RELEASE