Training speaker recognition systems with limited data

• URL: http://arxiv.org/abs/2203.14688v1
• Date: Mon, 28 Mar 2022 12:41:41 GMT
• Title: Training speaker recognition systems with limited data
• Authors: Nik Vaessen and David A. van Leeuwen
• Abstract summary: This work considers training neural networks for speaker recognition with a much smaller dataset size compared to contemporary work. We artificially restrict the amount of data by proposing three subsets of the popular VoxCeleb2 dataset. We show that the self-supervised, pre-trained weights of wav2vec2 substantially improve performance when training data is limited.
• Score: 2.3148470932285665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work considers training neural networks for speaker recognition with a much smaller dataset size compared to contemporary work. We artificially restrict the amount of data by proposing three subsets of the popular VoxCeleb2 dataset. These subsets are restricted to 50 k audio files (versus over 1 M files available), and vary on the axis of number of speakers and session variability. We train three speaker recognition systems on these subsets; the X-vector, ECAPA-TDNN, and wav2vec2 network architectures. We show that the self-supervised, pre-trained weights of wav2vec2 substantially improve performance when training data is limited. Code and data subsets are available at \url{https://github.com/nikvaessen/w2v2-speaker-few-samples}.

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