Title :
Analysis of compressed speech signals in an Automatic Speaker Recognition system
Author :
Metzger, Richard A. ; Doherty, John F. ; Jenkins, David M.
Author_Institution :
Dept. of Electr. Eng., Pennsylvania State Univ., State College, PA, USA
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.
Keywords :
Gaussian noise; Gaussian processes; audio coding; cepstral analysis; data compression; feature extraction; mixture models; speaker recognition; speech coding; Gaussian mixture model; MFCC; audio compression algorithms; automatic speaker recognition system; compressed speech signal analysis; feature extraction; mel-frequency cepstrum coefficients; white Gaussian noise; Compression algorithms; Digital audio players; Hidden Markov models; Signal to noise ratio; Speech; Speech recognition; Audio Compression; Gaussian Mixture Models; Mel-Frequency Cepstrum Coefficients; Speaker Recognition;
Conference_Titel :
Information Sciences and Systems (CISS), 2015 49th Annual Conference on
Conference_Location :
Baltimore, MD
DOI :
10.1109/CISS.2015.7086817
