Morrison Geoffrey Stewart
Forensic Speech Science Laboratory, Centre for Forensic Linguistics, Aston University, Birmingham, England, United Kingdom; Forensic Evaluation Ltd., Birmingham, England, United Kingdom.
Forensic Sci Int. 2018 Feb;283:e1-e7. doi: 10.1016/j.forsciint.2017.12.024. Epub 2017 Dec 19.
In a 2017 New South Wales case, a forensic practitioner conducted a forensic voice comparison using a Gaussian mixture model - universal background model (GMM-UBM). The practitioner did not report the results of empirical tests of the performance of this system under conditions reflecting those of the case under investigation. The practitioner trained the model for the numerator of the likelihood ratio using the known-speaker recording, but trained the model for the denominator of the likelihood ratio (the UBM) using high-quality audio recordings, not recordings which reflected the conditions of the known-speaker recording. There was therefore a difference in the mismatch between the numerator model and the questioned-speaker recording versus the mismatch between the denominator model and the questioned-speaker recording. In addition, the practitioner did not calibrate the output of the system. The present paper empirically tests the performance of a replication of the practitioner's system. It also tests a system in which the UBM was trained on known-speaker-condition data and which was empirically calibrated. The performance of the former system was very poor, and the performance of the latter was substantially better.
在2017年新南威尔士州的一个案例中，一名法医从业者使用高斯混合模型-通用背景模型（GMM-UBM）进行了法医语音比对。该从业者未报告在反映所调查案件条件的情况下对该系统性能进行实证测试的结果。该从业者使用已知说话者的录音训练似然比分子的模型，但使用高质量音频录音而非反映已知说话者录音条件的录音训练似然比分母的模型（通用背景模型）。因此，分子模型与受质疑说话者录音之间的不匹配与分母模型与受质疑说话者录音之间的不匹配存在差异。此外，该从业者未对系统输出进行校准。本文对该从业者系统的复制品性能进行了实证测试。它还测试了一个在已知说话者条件数据上训练通用背景模型并经过实证校准的系统。前一个系统的性能非常差，而后一个系统的性能则明显更好。
