The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains
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Abstract (arXiv)
We present the second edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthesized and processed speech. This year, we emphasize real-world and challenging zero-shot out-of-domain MOS prediction with three tracks for three different voice evaluation scenarios. Ten teams from industry and academia in seven different countries participated. Surprisingly, we found that the two sub-tracks of French text-to-speech synthesis had large differences in their predictability, and that singing voice-converted samples were not as difficult to predict as we had expected. Use of diverse datasets and listener information during training appeared to be successful approaches.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Oct 2023.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 1.
8 papers.
- 2024The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic Speechcited by 78
- 2023Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Predictioncited by 4
- 2024An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarioscited by 4
- 2025From Scores to Preferences: Redefining MOS Benchmarking for Speech Quality Reward Modelingcited by 4
- 2025Improving Perceptual Audio Aesthetic Assessment via Triplet Loss and Self-Supervised Embeddingscited by 1
- 2024Zero-Shot vs. Few-Shot Multi-Speaker TTS Using Pre-trained Czech SpeechT5 Modelcited by 0
- 2025Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditionscited by 0
- 2026DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localizationcited by 0
- 2026DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization21 Aug 2026
- 2025From Scores to Preferences: Redefining MOS Benchmarking for Speech Quality Reward Modeling1 Oct 2025
- 2025Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions29 Sep 2025
- 2025Improving Perceptual Audio Aesthetic Assessment via Triplet Loss and Self-Supervised Embeddings3 Sep 2025
- 2024The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic Speech14 Sep 2024
- 2024Zero-Shot vs. Few-Shot Multi-Speaker TTS Using Pre-trained Czech SpeechT5 Model24 Jul 2024
- 2024An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios13 Jun 2024
- 2023Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction25 Dec 2023