Analysis of Compressed Speech Signals in an Automatic Speaker Recognition System

被引：0

作者：

Metzger, Richard A. ^{[1
]}

Doherty, John F. ^{[1
]}

Jenkins, David M. ^{[2
]}

机构：

[1] Penn State Univ, Dept Elect Engn, University Pk, PA 16802 USA

[2] Appl Res Lab, University Pk, PA USA

来源：

2015 49TH ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS (CISS) | 2015年

关键词：

Speaker Recognition; Gaussian Mixture Models; Mel-Frequency Cepstrum Coefficients; Audio Compression;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper analyzes the effects popular audio compression algorithms have on the performance of a speaker recognition system. Popular audio compression algorithms were used to compress both clean and noisy speech before being passed to a speaker recognition system. The features extracted from each speaker were 19-dimensional Mel-Frequency Cepstrum Coefficients (MFCC) and the corresponding features were modeled using a 16 mixture Gaussian Mixture Model (GMM). Our experiments show that compression will have a negative effect on recognition rates if the compressed speech is clean. However, if small amounts of white Gaussian noise are added before the speech is compressed, recognition rates can be increased by as much as 7% with certain compression algorithms.

引用

页数：5

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