在巴西,出于法医目的,对说话人的识别仍然依赖于通过对不可信技术的结果分析基于主观性的决策过程。由于缺乏语音数据库,发言人验证目前适用于专门为对抗而收集的样本。然而,通过有争议的话语进行说话者比较分析需要为一系列个体收集过量的语音样本。Further,识别系统必须告知谁与预先选择的个人的有争议的声音最兼容。因此,本文提出了使用线性预测编码(LPC)和普通最小二乘(OLS)的组合作为取证分析的说话者验证工具。拟议的识别技术建立了作为法医报告基础的置信度和相似性,指示有争议的话语的说话者的验证。因此,在本文中,一个准确的,快,帮助验证说话者是否有贡献的替代方法。在运行了七个不同的测试之后,考虑到有限的数据集(巴西葡萄牙语),这项研究初步实现了100%的命中率。此外,所开发的方法提取了大量的共振峰,这对于通过OLS进行统计比较是必不可少的。所提出的框架在一定的噪声水平下是稳健的,对于抑制单词变化的句子,并且具有不同的质量甚至有意义的音频时间差。
In Brazil, the recognition of speakers for forensic purposes still relies on a subjectivity-based decision-making process through a results analysis of untrustworthy techniques. Owing to the lack of a voice database, speaker verification is currently applied to samples specifically collected for confrontation. However, speaker comparative analysis via contested discourse requires the collection of an excessive amount of voice samples for a series of individuals. Further, the recognition system must inform who is the most compatible with the contested voice from pre-selected individuals. Accordingly, this paper proposes using a combination of linear predictive coding (LPC) and ordinary least squares (OLS) as a speaker verification tool for forensic analysis. The proposed recognition technique establishes confidence and similarity upon which to base forensic reports, indicating verification of the speaker of the contested discourse. Therefore, in this paper, an accurate, quick, alternative method to help verify the speaker is contributed. After running seven different tests, this study preliminarily achieved a hit rate of 100% considering a limited dataset (Brazilian Portuguese). Furthermore, the developed method extracts a larger number of formants, which are indispensable for statistical comparisons via OLS. The proposed framework is robust at certain levels of noise, for sentences with the suppression of word changes, and with different quality or even meaningful audio time differences.