mlsanityData to 7 Oct 2026

SVSNet: An End-to-end Speaker Voice Similarity Assessment Model

arXiv 2107.09392 ↗First posted 20 Jul 2021eess.AS cs.LG cs.SDcited by 4

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Abstract (arXiv)

Neural evaluation metrics derived for numerous speech generation tasks have recently attracted great attention. In this paper, we propose SVSNet, the first end-to-end neural network model to assess the speaker voice similarity between converted speech and natural speech for voice conversion tasks. Unlike most neural evaluation metrics that use hand-crafted features, SVSNet directly takes the raw waveform as input to more completely utilize speech information for prediction. SVSNet consists of encoder, co-attention, distance calculation, and prediction modules and is trained in an end-to-end manner. The experimental results on the Voice Conversion Challenge 2018 and 2020 (VCC2018 and VCC2020) datasets show that SVSNet outperforms well-known baseline systems in the assessment of speaker similarity at the utterance and system levels.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 27 of its 49 references that are in the map. First posted on arXiv 20 Jul 2021.

Builds on

27 of 49 references.

References · 27

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15 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1.

4 papers.

  1. 2021Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Featurescited by 32
  2. 2025Analyzing and Improving Speaker Similarity Assessment for Speech Synthesiscited by 3
  3. 2022Automatic Evaluation of Speaker Similaritycited by 2
  4. 2024SVSNet+: Enhancing Speaker Voice Similarity Assessment Models with Representations from Speech Foundation Modelscited by 1