Cubic Law and MAP Compensation Techniques for Robust Text-Independent Speaker Identification

被引：0
|
作者：
Anacleto, Harry [1 ]
Chavez, David [1 ]
机构：
[1] Pontifical Catholic Univ Peru, Dept Engn, Lima 15088, Peru
来源：
PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP), 27TH EDITION | 2020年
关键词：
speaker identification; PNCC; cubic law; histogram mapping; noise robustness; NOISE;
D O I：
10.1109/iwssip48289.2020.9145319
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Automatic speaker recognition is drastically degraded in presence of noise. This paper focuses on the application of the cubic law and histogram mapping for the text-independent speaker recognition task. Our aim of this study is the application of these two methods in the feature extraction stage of the Power-Normalized Cepstral Coefficients (PNCC) and the conventional Mel Frequency Cepstral Coefficients (MFCC) techniques. Recognition results show that the cubic law combined with the histogram mapping improve the recognition rates.
引用
收藏
页码：387 / 392
页数：6
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