Maximum Likelihood A Priori Knowledge Interpolation-Based Handset Mismatch Compensation for Robust Speaker Identification |
| |
Authors: | Yuanfu Liao í Zhixian Zhuang
Jyhher Yang
|
| |
Institution: | aDepartment of Electronic Engineering, Taipei University of Technology, Taipei 106, China;bDepartment of Communication Engineering, Chiao Tung University, Hsinchu 300, China |
| |
Abstract: | Unseen handset mismatch is the major source of performance degradation in speaker identification in telecommunication environments. To alleviate the problem, a maximum likelihood a priori knowledge interpolation (ML-AKI)-based handset mismatch compensation approach is proposed. It first collects a set of handset characteristics of seen handsets to use as the a priori knowledge for representing the space of handsets. During evaluation the characteristics of an unknown test handset are optimally estimated by interpolation from the set of the a priori knowledge. Experimental results on the HTIMIT database show that the ML-AKI method can improve the average speaker identification rate from 60.0% to 74.6% as compared with conventional maximum a posteriori-adapted Gaussian mixture models. The proposed ML-AKI method is a promising method for robust speaker identification. |
| |
Keywords: | robust speaker identification maximum likelihood estimation handset mismatch compensation Gaussian mixture model maximum a posteriori |
本文献已被 维普 ScienceDirect 等数据库收录! |