Robust command recognition using kernel learning algorithms

Author

Narnarvar, H.H. ; Berger, Theodore W.

Author_Institution

Dept. of Biomed. Eng., Univ. of Southern California, Los Angeles, CA, USA

Volume

4

fYear

2003

fDate

20-24 July 2003

Firstpage

3134

Abstract

We introduce a new idea of robust command recognition system using frequency domain analysis and radial basis function support vector machines (SVM). We have tested the proposed system under stationary background noise hypothesis and have compared the performance of SVM classifier to multi-layer perceptron and radial basis function neural network classifiers. Simulations were carried out on the TI-46 corpus and higher performance of the SVM classifier was obtained on a small set of commands.

Keywords

frequency-domain analysis; radial basis function networks; signal classification; speech recognition; support vector machines; SVM classifier; TI-46 corpus; frequency domain analysis; kernel learning algorithms; multi-layer perceptron; neural network classifiers; noise robust speech recognition system; radial basis function support vector machines; robust command recognition; stationary background noise hypothesis; Acoustic noise; Additive noise; Automatic speech recognition; Background noise; Kernel; Robustness; Speech enhancement; Speech recognition; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1224073

Filename

1224073