@article{LIAO2008, 
author = {Yuanfu LIAO and Zhixian ZHUANG and Jyhher YANG},
title = {Maximum Likelihood A Priori Knowledge Interpolation-Based Handset Mismatch Compensation for Robust Speaker Identification},
year = {2008},
journal = {Tsinghua Science and Technology},
volume = {13},
number = {4},
pages = {528-532},
keywords = {Gaussian mixture model, maximum likelihood estimation, robust speaker identification, handset mismatch compensation, maximum a posteriori},
url = {https://www.sciopen.com/article/10.1016/S1007-0214(08)70084-1},
doi = {10.1016/S1007-0214(08)70084-1},
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.}
}