Classification of speech accents with neural networks

Author

Chan, Mike V. ; Feng, Xin ; Heinen, James A. ; Niederjohn, Russell J.

Author_Institution

Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA

Volume

7

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

4483

Abstract

Several neural network models including competitive learning and counter propagation are developed to identify individuals as either native or non-native speakers based on their accents. Some important speech features, such as pitch period and the first three formant frequencies, are used as inputs to the neural networks. Comparison results based on experiments are also presented. The primary contribution is that it provides a feasible approach for an assisting automatic speech recognition system in an environment in which different English accents may be used

Keywords

neural nets; pattern classification; speech recognition; unsupervised learning; English accents; automatic speech recognition system; competitive learning; counter propagation; formant frequencies; native speakers; neural networks; nonnative speakers; pitch period; speech accents classification; Artificial neural networks; Automatic speech recognition; Computer architecture; Counting circuits; Frequency; Neural networks; Pattern recognition; Speech processing; Speech recognition; Supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374994

Filename

374994