EURASIP Journal on Advances in Signal Processing
Simple Noise Robust Feature Vector Selection Method for Speaker Recognition
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Computers and Electrical Engineering
i-Vector with sparse representation classification for speaker verification
Speech Communication
VoCMex: a voice corpus in Mexican Spanish for research in speaker recognition
International Journal of Speech Technology
Hi-index | 0.00 |
This paper illustrates an evolution in state-of-the-art speaker verification by highlighting the contribution from newly developed techniques. Starting from a baseline system based on Gaussian mixture models that reached state-of-the-art performances during the NIST'04 SRE, final systems with new intersession compensation techniques show a relative gain of around 50%. This work highlights that a key element in recent improvements is still the classical maximum a posteriori (MAP) adaptation, while the latest compensation methods have a crucial impact on overall performances. Nuisance attribute projection (NAP) and factor analysis (FA) are examined and shown to provide significant improvements. For FA, a new symmetrical scoring (SFA) approach is proposed. We also show further improvement with an original combination between a support vector machine and SFA. This work is undertaken through the open-source ALIZE toolkit.