Constraint satisfaction model for enhancement of evidence in recognition of consonant-vowel utterances

Author

Gangashetty, Suryakanth V. ; Sekhar, C. Chandra ; Yegnanarayana, B.

Author_Institution

Dept. of Comput. Sci. & Eng., Indian Inst. of Technol. Madras, Chennai, India

Volume

2

fYear

2003

fDate

6-10 April 2003

Abstract

We address the issues in recognition of a large number of subword units of speech with high confusability among several units. Evidence available from the classification models trained with a limited number of training examples may not be strong to correctly recognize the subword units. We present a constraint satisfaction neural network model that can be used to enhance the evidence for a particular unit with the supporting evidence available for a subset of units confusable with that unit. We demonstrate the enhancement of evidence by the proposed model in recognition of utterances of 145 consonant-vowel units.

Keywords

neural nets; speech recognition; classification models; consonant-vowel units; consonant-vowel utterances recognition; constraint satisfaction model; constraint satisfaction neural network model; evidence enhancement; high confusability speech; speech subword units; subword units recognition; training examples; Background noise; Computer science; Feedforward neural networks; Intelligent networks; Laboratories; Multi-layer neural network; Natural languages; Neural networks; Speech enhancement; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-7663-3

Type

conf

DOI

10.1109/ICASSP.2003.1202476

Filename

1202476