# TidyVoice: A Curated Multilingual Dataset for Speaker Verification Derived from Common Voice

arXiv: https://arxiv.org/abs/2601.16358 · HTML: https://mlsanity.com/paper/2601.16358/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2026.

## Built on by (most cited first, 6 of 6)

- [What and When to Learn: CURriculum Ranking Loss for Large-Scale Speaker Verification](https://mlsanity.com/paper/2603.24432/) (2026)
- [Spoken Language Identification with Pre-trained Models and Margin Loss](https://mlsanity.com/paper/2605.01905/) (2026)
- [Building The Ph(ysical)AI Layer Of Machine Intelligence](https://mlsanity.com/paper/2606.04106/) (2026)
- [L-Proto: Language-Aware Episodic Prototypical Training for Multilingual Speaker Verification](https://mlsanity.com/paper/2606.17416/) (2026)
- [Simple Language Normalization Wins: Cross-Lingual Speaker Verification for the TidyVoice 2026 Challenge](https://mlsanity.com/paper/2607.22923/) (2026)
- [Transsion's Speaker-Attributed Multilingual ASR System for the MLC-SLM 2026 Challenge](https://mlsanity.com/paper/2609.20833/) (2026)

## Abstract (arXiv)

The development of robust, multilingual speaker recognition systems is hindered by a lack of large-scale, publicly available and multilingual datasets, particularly for the read-speech style crucial for applications like anti-spoofing. To address this gap, we introduce the TidyVoice dataset derived from the Mozilla Common Voice corpus after mitigating its inherent speaker heterogeneity within the provided client IDs. TidyVoice currently contains training and test data from over 212,000 monolingual speakers (Tidy-M) and around 4,500 multilingual speakers (Tidy-X) from which we derive two distinct conditions. The Tidy-M condition contains target and non-target trials from monolingual speakers across 81 languages. The Tidy-X condition contains target and non-target trials from multilingual speakers in both same- and cross-language trials. We employ two architectures of ResNet models, achieving a 0.35% EER by fine-tuning on our comprehensive Tidy-M partition. Moreover, we show that this fine-tuning enhances the model's generalization, improving performance on unseen conversational interview data from the CANDOR corpus. The complete dataset, evaluation trials, and our models are publicly released to provide a new resource for the community.