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arXiv · 2406.08445

SVSNet+: Enhancing Speaker Voice Similarity Assessment Models with Representations from Speech Foundation Models

Chun Yin·Tai-Shih Chi·Yu Tsao·Hsin-Min Wang
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Abstract

Representations from pre-trained speech foundation models (SFMs) have shown impressive performance in many downstream tasks. However, the potential benefits of incorporating pre-trained SFM representations into speaker voice similarity assessment have not been thoroughly investigated. In this paper, we propose SVSNet+, a model that integrates pre-trained SFM representations to improve performance in assessing speaker voice similarity. Experimental results on the Voice Conversion Challenge 2018 and 2020 datasets show that SVSNet+ incorporating WavLM representations shows significant improvements compared to baseline models. In addition, while fine-tuning WavLM with a small dataset of the downstream task does not improve performance, using the same dataset to learn a weighted-sum representation of WavLM can substantially improve performance. Furthermore, when WavLM is replaced by other SFMs, SVSNet+ still outperforms the baseline models and exhibits strong generalization ability.

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Chun Yin, Tai-Shih Chi, Yu Tsao, Hsin-Min Wang. 2024-06-12. SVSNet+: Enhancing Speaker Voice Similarity Assessment Models with Representations from Speech Foundation Models. https://arxiv.org/abs/2406.08445

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