[Invited Speaker] Building privacy-aware large-scale speech datasets through generative modeling

講演者：Junichi Yamagishi
会議名：Frontier Forum on Intelligent Speech Analysis and Generation
主催者：The University of Science and Technology of China (USTC)
開催地：安徽, 中国
開催日：2024年7月5日
URL：https://nelslip.ustc.edu.cn/2024/0709/c26914a646967/page.htm

The success of deep learning in speech and speaker recognition relies heavily on the use of large datasets. However, ethical, privacy and legal concerns also arise when using large datasets of speech collected from real human speech data. In particular, there are significant concerns in this regard when collecting large number of speaker’s speech data from the web.

On the other hand, the quality of synthesised speech produced by recent generative models is very high. Is it possible to ‘generate’ large, privacy-aware, unbiased and fair datasets with speech generative models?  Such studies have started not only for speech datasets but also for facial image datasets.

In this talk, I will introduce our efforts to construct a synthetic VoxCeleb2 dataset called SynVox2 that is speaker-anonymised and privacy-aware. In addition to the procedures and methods used in the construction, the challenges and problems of using synthetic data will be discussed by showing the performance and fairness of a speaker verification system built using the SynVox2 database.