251 research outputs found
Disentangled representation learning for multilingual speaker recognition
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
language-disentangled learning method only uses language pseudo-labels without
manual information.Comment: Interspeech 202
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