Neural network models for preprocessing and discriminating utterances of consonant-vowel units

Author

Gangashetty, Suryakanth V. ; Khan, A. Nayeemulla ; Prasanna, S. R Mahadeva ; Yegnanarayana, B.

Author_Institution

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

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

613

Lastpage

618

Abstract

We demonstrate the significance of nonlinear neural network models for compression of feature vectors and also develop classifiers for syllable-like units. We consider the standard 80 stop consonant-vowel units of most Indian languages. This set consists of dynamic sounds and hence requires large size feature vectors to represent the acoustic characteristics of these units. To develop classifiers with limited training data, it is necessary to compress the size of the feature vector. We show that nonlinear compression by autoassociative neural network model is useful, and is superior to the compression by linear principal component analysis

Keywords

data compression; feedforward neural nets; multilayer perceptrons; pattern classification; speech recognition; Indian languages; acoustic characteristics; autoassociative neural network model; consonant-vowel units; dynamic sounds; feature vectors compression; neural network models; nonlinear compression; nonlinear neural network models; syllable-like units; utterances discrimination; utterances preprocessing; Computer science; Laboratories; Multi-layer neural network; Natural languages; Neural networks; Principal component analysis; Production; Speech; Training data; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005542

Filename

1005542