TidyVoice: A Curated Multilingual Dataset for Speaker Verification Derived from Common Voice
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the TidyVoice dataset, a large-scale multilingual resource for speaker verification, which addresses significant gaps in existing datasets. This work is a notable advancement in the field, providing essential resources for developing more inclusive and effective speaker recognition systems.
Methodology
The paper presents a well-structured methodology for curating the TidyVoice dataset from the Mozilla Common Voice corpus, addressing critical issues of speaker heterogeneity through a verification-based pipeline. The use of ResNet architectures for speaker verification is appropriate, and the fine-tuning process is clearly articulated, demonstrating a systematic approach to enhancing model performance. However, the paper could benefit from more detailed explanations on hyperparameter tuning and the rationale behind the chosen architectures.
Results & evaluation
The experimental evaluation is robust, with comprehensive results across multiple benchmarks, including the CANDOR corpus. The authors effectively demonstrate the performance improvements achieved through fine-tuning on the TidyVoice dataset. The use of standard metrics (EER and minDCF) is appropriate, and the results are presented clearly. However, the paper could provide more context on the significance of the improvements in practical applications.
Reproducibility
The authors have made a commendable effort to ensure reproducibility by publicly releasing the dataset, evaluation trials, and trained models. The detailed description of the experimental setup, including feature extraction and model training protocols, enhances the reproducibility of their findings. However, the paper could include more information on the specific versions of libraries and frameworks used.
Limitations
One limitation is the reliance on the Mozilla Common Voice corpus, which may still contain noise and inconsistencies despite the authors' efforts to mitigate speaker heterogeneity. Additionally, the dataset's performance may vary significantly across languages, particularly those with fewer speakers. The paper also does not address potential biases in the dataset that could affect model generalization.
Broader impact
The TidyVoice dataset has the potential to significantly advance the field of multilingual speaker verification, particularly in applications requiring anti-spoofing measures. By providing a large-scale, publicly available resource, the authors contribute to fairer and more robust speaker recognition systems. The implications of this work extend to security applications, personalized user experiences, and accessibility in technology. The main contribution of this paper is the introduction of the TidyVoice dataset, a large-scale multilingual resource for speaker verification, which addresses significant gaps in existing datasets. This work is a notable advancement in the field, providing essential resources for developing more inclusive and effective speaker recognition systems.
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