Speech security on human-computer interaction

在本财年，我们提出了基于时间尺度修改（TSM）算法的说话人匿名化方法。研究发现基于相位声码器的TSM方法由于人声的谐波结构更适合于说话人匿名化。所提出的方法比基线系统更好地平衡了隐私和效用指标。此外，我们还利用基于x向量说话人嵌入的性别分类器模型分析了匿名化对性别感知的影响。我们的研究结果在2022年语音隐私挑战、Interspeech 2022会议和亚太信号与信息处理协会2022年组织的第14届年度会议上发表。

In this fiscal year, we proposed speaker anonymization methods based on time scale modification (TSM) algorithms. The study finds that using the phase vocoder-based TSM method is more suitable for speaker anonymization due to the human voice's harmonic structures. The proposed method balances privacy and utility metrics better than baseline systems. Besides, we also analyzed the effect of anonymization on the perception of gender by utilizing a gender classifier model that was built using x-vector speaker embedding. The results of our study were presented at the Voice Privacy Challenge 2022, joint with the Interspeech 2022 conference and the 14th annual conference organized by Asia-Pacific Signal and Information Processing Association 2022.