AI in EE

AI IN DIVISIONS

AI in Signal Division

AI in EE

AI IN DIVISIONS

AI in Signal Division ​ ​

AI in Signal Division

Disentangled Representation Learning for Multilingual Speaker Recognition (정준선 교수 연구실)

Title: Disentangled Representation Learning for Multilingual Speaker Recognition

Authors: K. Nam, Y. Kim, J. Huh, H. Heo, J. Jung, J. S. Chung

Conference: Interspeech

Abstract: The goal of this paper is to learn robust speaker representation for bilingual speaking scenario. The majority of the world’s population speak at least two languages; however, most speaker recognition systems fail to recognise the same speaker when speaking in different languages. Popular speaker recognition evaluation sets do not consider the bilingual scenario, making it difficult to analyse the effect of bilingual speakers on speaker recognition performance. In this paper, we publish a large-scale evaluation set named VoxCeleb1-B derived from VoxCeleb that considers bilingual scenarios. We introduce an effective disentanglement learning strategy that combines adversarial and metric learning-based methods. This approach addresses the bilingual situation by disentangling language-related information from speaker representation while ensuring stable speaker representation learning. Our languagedisentangled learning method only uses language pseudo-labels without manual information.

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