SEA-Spoof: Bridging The Gap in Multilingual Audio Deepfake Detection for South-East Asian
Abstract
The rapid growth of the digital economy in South-East Asia (SEA) has amplified the risks of audio deepfakes, yet current datasets cover SEA languages only sparsely, leaving models poorly equipped to handle this critical region. This omission is critical: detection models trained on high-resource languages collapse when applied to SEA, due to mismatches in synthesis quality, language-specific characteristics, and data scarcity. To close this gap, we present SEA-Spoof, the first large-scale Audio Deepfake Detection (ADD) dataset especially for SEA languages. SEA-Spoof spans 300+ hours of paired real and spoof speech across Tamil, Hindi, Thai, Indonesian, Malay, and Vietnamese. Spoof samples are generated from a diverse mix of state-of-the-art open-source and commercial systems, capturing wide variability in style and fidelity. Benchmarking state-of-the-art detection models reveals severe cross-lingual degradation, but fine-tuning on SEA-Spoof dramatically restores performance across languages and synthesis sources. These results highlight the urgent need for SEA-focused research and establish SEA-Spoof as a foundation for developing robust, cross-lingual, and fraud-resilient detection systems.
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 presents SEA-Spoof, the first large-scale dataset for audio deepfake detection in six South-East Asian languages, filling a critical gap in existing resources and demonstrating significant improvements in detection performance through fine-tuning. The comprehensive methodology and experimental validation highlight its importance for advancing research in multilingual audio deepfake detection.
Methodology
The methodology is robust, focusing on the creation of the SEA-Spoof dataset, which is a significant contribution to the field of audio deepfake detection. The authors carefully selected six South-East Asian languages based on linguistic diversity, population coverage, and practical relevance. The dataset construction is thorough, utilizing a mix of state-of-the-art open-source and commercial systems to generate spoofed audio, which ensures a wide variability in synthesis quality. The systematic pairing of real and spoofed audio for controlled evaluations is a strong methodological aspect that enhances the dataset's utility for future research.
Results & evaluation
The experimental evaluation is comprehensive, benchmarking multiple state-of-the-art models against the newly created SEA-Spoof dataset. The results clearly demonstrate the cross-lingual performance degradation of existing models when applied to SEA languages, validating the necessity of the dataset. Fine-tuning experiments show significant improvements in model performance, underscoring the dataset's effectiveness as a diagnostic tool and a resource for enhancing detection capabilities.
Reproducibility
The paper provides sufficient details on the dataset's construction and the experimental setup, including the models used for benchmarking and the training protocols. However, the lack of a publicly available code repository limits the full reproducibility of the experiments. While the dataset is accessible, the absence of implementation details for the models may hinder other researchers from replicating the study completely.
Limitations
One limitation is the focus on only six languages, which, while significant, does not cover the entire spectrum of languages in the SEA region. Additionally, the dataset's reliance on specific synthesis systems may introduce biases that could affect generalizability. The paper also mentions plans for future work, indicating that the dataset may evolve, but the current version may not be exhaustive.
Broader impact
The creation of SEA-Spoof has the potential to significantly impact the field of audio deepfake detection, particularly in multilingual contexts. By addressing the gap in resources for SEA languages, the dataset can facilitate the development of more effective detection systems tailored to the unique characteristics of these languages. This work emphasizes the importance of regional focus in AI research and could lead to broader applications in security, fraud detection, and speech technology. The paper presents SEA-Spoof, the first large-scale dataset for audio deepfake detection in six South-East Asian languages, filling a critical gap in existing resources and demonstrating significant improvements in detection performance through fine-tuning. The comprehensive methodology and experimental validation highlight its importance for advancing research in multilingual audio deepfake detection.
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