Comparative Study of Several Novel Acoustic Features for Speaker Recognition

Author(s)
Pervouchine, Vladimir
Leedham, Graham
Zhong, Haishan
Cho, David
Li, Haizhou
Publication Date
2008
Abstract
Finding good features that represent speaker identity is an important problem in speaker recognition area. Recently a number of new and novel acoustic features have been proposed for speaker recognition. The researchers use different data sets and sometimes different classifiers to evaluate the features and compare them to the baselines such as MFCC or LPCC. However, due to different experimental conditions direct comparison of those features to each other is difficult or impossible. This paper presents a study of five new acoustic features recently proposed. The feature extraction has been performed on the same data (NIST~2001~SRE), and the same UBM-GMM classifier has been used. The results are presented as DET curves with equal error ratios indicated. Also, an SVM-based combination of GMM scores produced on different features has been made in hope that classifier fusion can result in higher speaker recognition accuracy. The results for different features as well as for their combinations are directly comparable to each other and to those obtained with the baseline MFCC features.
Citation
Proceedings of the First International Conference on Biomedical Electronics and Devices (BIOSIGNALS 2008), v.1, p. 220-223
ISBN
9789898111180
Link
Publisher
Institute for Systems and Technologies of Information, Control and Communication (INSTICC)
Title
Comparative Study of Several Novel Acoustic Features for Speaker Recognition
Type of document
Conference Publication
Entity Type
Publication

Files:

NameSizeformatDescriptionLink