COMPARING MULTILAYER PERCEPTRONS AND RADIAL BASIS FUNCTIONS NETWORKS IN SPEAKER RECOGNITION

被引：3

作者：

MAK, MW ^{[1
]}

ALLEN, WG ^{[1
]}

SEXTON, GG ^{[1
]}

机构：

[1] UNIV NORTHUMBRIA,DEPT ELECT ELECTR ENGN & PHYS,NEWCASTLE,ENGLAND

来源：

JOURNAL OF MICROCOMPUTER APPLICATIONS | 1993年 / 16卷 / 02期

关键词：

D O I：

10.1006/jmca.1993.1013

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We have compared the performance of Multi-layer Perceptrons networks (MLP) and Radial Basis Function networks (RBF) in the task of speaker identification. The experiments are carried out on 400 utterances (10 digits, in English) from 10 speakers. LPC-derived Cepstrum Coefficients are used as the speaker specific features. The results show that the MLP networks are superior in memory usage and classification time. Nevertheless, they suffer from long training time and the classification performance is poorer than that of the RBF networks. The function centres of the RBF networks are either selected randomly from the training data or located by a K-mean algorithm. We find that K-mean clusteirng is an effective method in locating the function centres. We also find that by guaranteeing every speaker has similar number of function centres, the recognition performance can be improved further. © 1993 Academic Press. All rights reserved.

引用

页码：147 / 159

页数：13

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