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arXiv · 2509.00230

Evaluating the Effectiveness of Transformer Layers in Wav2Vec 2.0, XLS-R, and Whisper for Speaker Identification Tasks

Linus Stuhlmann·Michael Alexander Saxer
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Abstract

This study evaluates the performance of three advanced speech encoder models, Wav2Vec 2.0, XLS-R, and Whisper, in speaker identification tasks. By fine-tuning these models and analyzing their layer-wise representations using SVCCA, k-means clustering, and t-SNE visualizations, we found that Wav2Vec 2.0 and XLS-R capture speaker-specific features effectively in their early layers, with fine-tuning improving stability and performance. Whisper showed better performance in deeper layers. Additionally, we determined the optimal number of transformer layers for each model when fine-tuned for speaker identification tasks.

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Linus Stuhlmann, Michael Alexander Saxer. 2025-08-29. Evaluating the Effectiveness of Transformer Layers in Wav2Vec 2.0, XLS-R, and Whisper for Speaker Identification Tasks. https://arxiv.org/abs/2509.00230

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