Speaker verification: minimizing the channel effects using autoassociative neural network models

Author

Kishore, S.P. ; Yegnanarayana, B.

Author_Institution

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

Volume

2

fYear

2000

fDate

2000

Abstract

The characteristics of the telephone channel and handset have a significant effect on the performance of speaker verification systems. The channel/handset mismatch between the training and testing data degrades the performance of speaker verification systems. In this paper, we show that the autoassociative neural network (AANN) models can be used to minimize the effects of channel characteristics on the performance of a text-independent speaker verification system. This paper also compares two approaches to represent the background model for an AANN based speaker verification system

Keywords

feedforward neural nets; speaker recognition; telecommunication channels; telephone sets; AANN model; autoassociative neural network models; channel effects; channel/handset mismatch; handset; performance; speaker verification; telephone channel; text-independent speaker verification system; Bandwidth; Computer science; Databases; Degradation; Electronic mail; Feedforward neural networks; Neural networks; System testing; Telephone sets; Telephony;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on

Conference_Location

Istanbul

ISSN

1520-6149

Print_ISBN

0-7803-6293-4

Type

conf

DOI

10.1109/ICASSP.2000.859156

Filename

859156