A speaker recognition method based on GMM using non -negative matrix factorization

被引：0
|
作者：
Huang, Liming [1 ]
Liu, Dongbo [1 ]
Fang, Yu [1 ]
Wang, Weibo [1 ]
机构：
[1] Xi Hua Univ, Sch Elect & Elect Informat, Chengdu, Peoples R China
来源：
2022 IEEE 17TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA) | 2022年
基金：
中国国家自然科学基金;
关键词：
NMF; speaker recognition; GYM; feature fusion; ALGORITHMS;
D O I：
10.1109/ICIEA54703.2022.10006158
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Speaker recognition enhances communication efficiency based on dialogue. In this paper, we propose a speaker recognition method based on feature fusion with non negative matrix decomposition (NMF). It uses NMF to decompose the speaker's speech spectrogram, extract features from the decomposed basis matrix, and perform feature fusion with Mel-frequency cepstral coefficients (MFCC). Experimental results show that the fused features improve speaker recognition accuracy in low signal-to-noise (SNR) environment. Comparison with other speaker recognition methods using MFCC features.
引用
收藏
页码：870 / 875
页数：6
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