Pervouchine, Vladimir

Leedham, Graham

Zhong, Haishan

Cho, David

Li, Haizhou

2008

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.

Proceedings of the First International Conference on Biomedical Electronics and Devices (BIOSIGNALS 2008), v.1, p. 220-223

9789898111180

https://hdl.handle.net/1959.11/15313

en

Institute for Systems and Technologies of Information, Control and Communication (INSTICC)

Comparative Study of Several Novel Acoustic Features for Speaker Recognition

Conference Publication

Publication