Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: SVSNet+: Enhancing Speaker Voice Similarity Assessment Models with Representations from Speech Foundation Models
summary: 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.
Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: SVSNet+: Enhancing Speaker Voice Similarity Assessment Models with Representations from Speech Foundation Models
summary: 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.
id: http://arxiv.org/abs/2406.08445v1
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