摘要
The session variability is the most important factor affecting the performance of the speaker verification systems. In order to deal with the variability more efficiently, this paper provides a practical procedure for applying a smooth Within-Class Covariance Normalization (WCCN) to an SVM-based speaker verification system, where the dimension of input samples resides in a low Session-Invariant Principal Component Analysis(SIPCA) feature space. When the SIPCA and smooth WCCN approaches are implemented on NIST 2006 verification task, experimental results show relative reductions of up to 19.7% in EER and 18.4% in minimum decision cost Junction(DCF) over our previous GMM-mean SVM system. Our approach also has advantages in computational and memory costs compared to the state-of-art systems.
- 出版日期2008
- 单位中国科学技术大学