Independent component analysis applied to feature extraction for robust automatic speech recognition

被引：20

作者：

Potamitis, L ^{[1
]}

Fakotakis, N ^{[1
]}

Kokkinakis, G ^{[1
]}

机构：

[1] Univ Patras, Dept Elect & Comp Engn, Wire COmmun Lab, GR-26110 Patras, Greece

来源：

ELECTRONICS LETTERS | 2000年 / 36卷 / 23期

关键词：

D O I：

10.1049/el:20001365

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The authors explore independent component analysis (ICA) as a statistical technique for deriving suitable data-driven representational bases for the projection of spectra and cepstra in the context of automatic speech recognition (ASR). Based on the close link between the independent mechanisms of speech variability and the concept of statistical independence they derive a new feature transformation that effects consistent improvement in recognition performance.

引用

页码：1977 / 1978

页数：2