mlsanityData to 7 Oct 2026

Training speaker recognition systems with limited data

arXiv 2203.14688 ↗First posted 28 Mar 2022cs.SD cs.LG eess.AScited by 1

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Abstract (arXiv)

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 https://github.com/nikvaessen/w2v2-speaker-few-samples.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 28 Mar 2022.

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29 of 41 references.

References · 29

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17 more
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