关键词: 似然比, 证据强度, 建模, 梅尔频率倒谱系数（MFCC）, 伽马通频率倒谱系数（GFCC）

Abstract: Focusing on the lack of voice samples of a person to be examined in forensic speaker recognition, this paper proposes a new alternative method modeling the self-variability of target speaker and corresponding variance control algorithm. The method constructs multiple same-speaker scores of recognition system from a reference database under similar condition to take the place of multiple non-contemporaneous voice samples needed in examinations. The aim is to obtain the statistical model that can reflect the self-variability of the target speaker. MFCC and GFCC are used to test the performance of the proposed method in state-of-art evidence estimation framework based on likelihood ratio, and feature fusion and decision fusion are also been applied in the experiment. Results show that the proposed method has a very high rate of recognition and stability under the condition of clean voice and noisy voice, and feature fusion can further improve recognition performance.

Key words: likelihood ratio, evidence strength, modeling, Mel Frequency Cepstral Coefficients（MFCC）, Gammatone Frequency Cepstral Coefficients（GFCC）