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）

摘要： 针对法庭说话人识别中待鉴定人员语音样本不足的问题，提出了一种新的对说话人自身变化性建模的替代性方法以及相应的方差控制算法。使用同条件下的参考数据库构建识别系统的多个相同说话人得分模型，代替检验需要的多个非同期的带检验人员语音样本比较时的得分模型，以获得能反映说话人自身变化性的统计模型。基于目前最新的法庭证据评估的似然比证据强度评估体系，使用MFCC（Mel Frequency Cepstral Coefficients）和GFCC（Gammatone Frequency Cepstral Coefficients）特征对该方法的有效性进行了验证，并对上述特征进行了特征级和决策级融合。实验结果表明：该方法在纯净语音环境和噪声环境下都具有很高的识别率和稳定性，并且特征级融合能进一步提高识别系统的性能。

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）