LJ-Spoof: A Generatively Varied Corpus for Audio Anti-Spoofing and Synthesis Source Tracing
Abstract
Speaker-specific anti-spoofing and synthesis-source tracing are central challenges in audio anti-spoofing. Progress has been hampered by the lack of datasets that systematically vary model architectures, synthesis pipelines, and generative parameters. To address this gap, we introduce LJ-Spoof, a speaker-specific, generatively diverse corpus that systematically varies prosody, vocoders, generative hyperparameters, bona fide prompt sources, training regimes, and neural post-processing. The corpus spans one speakers-including studio-quality recordings-30 TTS families, 500 generatively variant subsets, 10 bona fide neural-processing variants, and more than 3 million utterances. This variation-dense design enables robust speaker-conditioned anti-spoofing and fine-grained synthesis-source tracing. We further position this dataset as both a practical reference training resource and a benchmark evaluation suite for anti-spoofing and source tracing.
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 paper introduces the LJ-Spoof dataset, a comprehensive resource for audio anti-spoofing research, systematically varying multiple parameters to enhance the robustness of speaker-specific detection systems. This work significantly contributes to the field by addressing the lack of diverse datasets and providing a structured methodology for future research.
Methodology
The methodology presented in the paper is robust and comprehensive, focusing on the creation of a diverse corpus that systematically varies multiple parameters relevant to audio anti-spoofing. The authors clearly outline the dataset generation process, categorizing variations by training regimes, input sources, generative parameters, and neural post-processing techniques. This structured approach allows for a nuanced analysis of the effects of these variations on speaker-specific anti-spoofing systems. The inclusion of a reproducible protocol enhances the methodological rigor, ensuring that other researchers can replicate the dataset generation process.
Results & evaluation
The paper does not present experimental results directly within the text, as it primarily focuses on the dataset creation. However, the authors emphasize the potential for the LJ-Spoof dataset to facilitate future experiments and evaluations of anti-spoofing systems. The dataset's scale, with over 3 million utterances and 500 generatively variant subsets, suggests that it can support extensive testing and validation of various models. The authors also propose a strategic split protocol for training and evaluating anti-spoofing systems, which is a crucial aspect for ensuring the dataset's effectiveness.
Reproducibility
The authors provide a detailed versioned protocol file that documents the architectural choices, configuration settings, and neural post-processing steps used in generating the dataset. This level of detail is commendable and significantly enhances the reproducibility of the research. By making the dataset open-sourced, the authors further promote transparency and community engagement, allowing other researchers to build upon their work.
Limitations
One limitation noted is the focus on a single speaker, which may restrict the generalizability of findings to multi-speaker scenarios. While the authors acknowledge this and express intentions to extend the corpus to include multiple speakers in the future, the current dataset may not fully address the complexities involved in real-world applications where multiple voices and contexts are present. Additionally, the reliance on existing TTS systems and their configurations may introduce biases based on the limitations of those systems.
Broader impact
The LJ-Spoof dataset has significant implications for the fields of audio processing, anti-spoofing, and synthetic speech detection. By providing a comprehensive resource for evaluating speaker-specific anti-spoofing systems, the dataset can help advance research aimed at mitigating the risks associated with voice cloning and synthetic speech technologies. This is particularly relevant in an era where deepfake technologies pose threats to personal security and misinformation. The potential for broader applications in security, telecommunications, and digital forensics is substantial. The paper introduces the LJ-Spoof dataset, a comprehensive resource for audio anti-spoofing research, systematically varying multiple parameters to enhance the robustness of speaker-specific detection systems. This work significantly contributes to the field by addressing the lack of diverse datasets and providing a structured methodology for future research.
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