Word recognition based on the combination of a sequential neural network and the GPDM discriminative training algorithm

Wen Yuan Chen*, Sin-Horng Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The authors propose an isolated-word recognition method based on the combination of a sequential neural network and a discriminative training algorithm using the Generalized Probabilistic Descent Method (GPDM). The sequential neural network deals with the temporal variation of speech by dynamic programming, and the GPDM discriminative training algorithm is used to discriminate easily confused words by enhancing the distinguishing sounds of them during the scoring procedure. A Mandarin digit database uttered by 100 speakers was used to evaluate the performance of this method. The recognition rates are 99.1% on training data and 96.3% on testing data.

Original languageEnglish
Title of host publicationNeural Networks for Signal Processing
PublisherPubl by IEEE
Pages376-384
Number of pages9
ISBN (Print)0780301188
DOIs
StatePublished - 1 Jun 1991
EventProceedings of the 1991 Workshop on Neural Networks for Signal Processing - NNSP-91 - Princeton, NJ, USA
Duration: 30 Sep 19912 Oct 1991

Publication series

NameNeural Networks for Signal Processing

Conference

ConferenceProceedings of the 1991 Workshop on Neural Networks for Signal Processing - NNSP-91
CityPrinceton, NJ, USA
Period30/09/912/10/91

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