The Multi-speaker Multi-style Voice Cloning Challenge 2021
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Abstract (arXiv)
The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide a common sizable dataset as well as a fair testbed for the benchmarking of the popular voice cloning task. Specifically, we formulate the challenge to adapt an average TTS model to the stylistic target voice with limited data from target speaker, evaluated by speaker identity and style similarity. The challenge consists of two tracks, namely few-shot track and one-shot track, where the participants are required to clone multiple target voices with 100 and 5 samples respectively. There are also two sub-tracks in each track. For sub-track a, to fairly compare different strategies, the participants are allowed to use only the training data provided by the organizer strictly. For sub-track b, the participants are allowed to use any data publicly available. In this paper, we present a detailed explanation on the tasks and data used in the challenge, followed by a summary of submitted systems and evaluation results.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2021.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 0; 2024: 0; 2025: 1.
7 papers.
- 2021A Survey on Neural Speech Synthesiscited by 182
- 2022Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing modulecited by 8
- 2025Analyzing and Improving Speaker Similarity Assessment for Speech Synthesiscited by 3
- 2022Self-supervised learning for robust voice cloningcited by 2
- 2021The AS-NU System for the M2VoC Challengecited by 1
- 2022Towards Cross-speaker Reading Style Transfer on Audiobook Datasetcited by 0
- 2022Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesiscited by 0
- 2025Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis2 Jul 2025
- 2022Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis14 Oct 2022
- 2022Towards Cross-speaker Reading Style Transfer on Audiobook Dataset10 Aug 2022
- 2022Self-supervised learning for robust voice cloning7 Apr 2022
- 2022Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module16 Feb 2022
- 2021A Survey on Neural Speech Synthesis29 Jun 2021
- 2021The AS-NU System for the M2VoC Challenge7 Apr 2021