Advances in Deep Speaker Verification

Title:

Advances in Deep Speaker Verification

Abstract:

Automatic speaker verification has numerous studies widely used in applications such as user authentication, access control, and smart assistants. Empowered by stronger hardware utilities, modern speaker verification systems based on deep neural networks have been moving away from conventional statistical models. However, their generalizability in realistic application scenarios remains challenging due to a number of nuisance variations resulting from both the environment and the speakers themselves. In addition, the increasing demands for offline usage on limited computational conditions and security need to be addressed as well. This dissertation advances modern text-independent speaker verification systems. It introduces multiple methods to make the speaker verification systems more robust to variations, usable with limited computational resources, and secure against audio spoofing attacks. The results can be utilized both in basic research and practical applications of speaker verification technology.

Presenter: Xuechen Liu

Location: NII 1512

Time: 2023-06-19 11:00 ~ 12:00 (JST time)

Other information:

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