AI in EE

AI IN DIVISIONS

AI in Signal Division

AI in EE

AI IN DIVISIONS

AI in Signal Division ​ ​

AI in Signal Division

Curriculum learning for self-supervised speaker verification (정준선 교수 연구실)

Title: Curriculum learning for self-supervised speaker verification

Authors: H. Heo, J. Jung, J. Kang, Y. Kwon, B. Lee, Y. J. Kim, J. S. Chung

Conference: Interspeech

Abstract: The goal of this paper is to train effective self-supervised speaker representations without identity labels. We propose two curriculum learning strategies within a self-supervised learning framework. The first strategy aims to gradually increase the number of speakers in the training phase by enlarging the used portion of the train dataset. The second strategy applies various data augmentations to more utterances within a mini-batch as the training proceeds. A range of experiments conducted using the DINO self-supervised framework on the VoxCeleb1 evaluation protocol demonstrates the effectiveness of our proposed curriculum learning strategies. We report a competitive equal error rate of 4.47% with a single-phase training, and we also demonstrate that the performance further improves to 1.84% by f ine-tuning on a small labelled dataset.

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