@inproceedings{ecb13a281351494f9456c6d343504cb8,
title = "Analysis of compressed speech signals in an Automatic Speaker Recognition system",
abstract = "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.",
author = "Metzger, {Richard A.} and Doherty, {John F.} and Jenkins, {David M.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 49th Annual Conference on Information Sciences and Systems, CISS 2015 ; Conference date: 18-03-2015 Through 20-03-2015",
year = "2015",
month = apr,
day = "15",
doi = "10.1109/CISS.2015.7086817",
language = "English (US)",
series = "2015 49th Annual Conference on Information Sciences and Systems, CISS 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 49th Annual Conference on Information Sciences and Systems, CISS 2015",
address = "United States",
}