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.

翻译：鲁棒的多语言说话人识别系统的发展受到缺乏大规模、公开可用的多语言数据集的阻碍，特别是对于反欺骗等应用至关重要的朗读语音风格。为弥补这一空白，我们在缓解了Mozilla Common Voice语料库中提供的客户端ID内固有的说话人异质性后，引入了由此衍生的TidyVoice数据集。TidyVoice目前包含来自超过212,000名单语说话人（Tidy-M）和约4,500名多语说话人（Tidy-X）的训练和测试数据，并从中衍生出两种不同的条件。Tidy-M条件包含来自81种语言的单语说话人的目标和非目标试验。Tidy-X条件包含来自多语说话人的目标和非目标试验，涵盖同语言和跨语言试验。我们采用了两种ResNet模型架构，通过在我们全面的Tidy-M分区上进行微调，实现了0.35%的等错误率。此外，我们证明这种微调增强了模型的泛化能力，提高了在CANDOR语料库中未见过的对话访谈数据上的性能。完整的数据集、评估试验以及我们的模型均已公开发布，为研究社区提供了一个新的资源。