MultiAPI Spoof: A Multi-API Dataset and Local-Attention Network for Speech Anti-spoofing Detection
Abstract
Existing speech anti-spoofing benchmarks rely on a narrow set of public models, creating a substantial gap from real-world scenarios in which commercial systems employ diverse, often proprietary APIs. To address this issue, we introduce MultiAPI Spoof, a multi-API audio anti-spoofing dataset comprising about 230 hours of synthetic speech generated by 30 distinct APIs, including commercial services, open-source models, and online platforms. Based on this dataset, we define the API tracing task, enabling fine-grained attribution of spoofed audio to its generation source. We further propose Nes2Net-LA, a local-attention enhanced variant of Nes2Net that improves local context modeling and fine-grained spoofing feature extraction. Experiments show that Nes2Net-LA achieves state-of-the-art performance and offers superior robustness, particularly under diverse and unseen spoofing conditions. Code \footnote{https://github.com/XuepingZhang/MultiAPI-Spoof} and dataset \footnote{https://xuepingzhang.github.io/MultiAPI-Spoof-Dataset/} have released.
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 a comprehensive approach to enhancing speech anti-spoofing detection through the introduction of a diverse dataset and a novel model architecture. This work is a meaningful contribution to the field, addressing real-world challenges and setting a foundation for future research in audio security.
Methodology
The paper introduces a novel dataset, MultiAPI Spoof, which significantly expands the scope of audio anti-spoofing research by incorporating 230 hours of synthetic speech from 30 different APIs. This approach addresses the limitations of existing datasets that primarily focus on a narrow set of models, thereby enhancing the realism and applicability of anti-spoofing detection methods. The proposed Nes2Net-LA model, which integrates local attention mechanisms into the existing Nes2Net architecture, is a meaningful advancement that improves local context modeling and feature extraction. The methodology is well-structured, with clear definitions of tasks and systematic enhancements to existing models.
Results & evaluation
The experiments conducted are comprehensive, utilizing multiple datasets to evaluate the performance of the proposed methods. The results demonstrate significant improvements in anti-spoofing detection and API tracing tasks, particularly in unseen scenarios. The paper provides detailed metrics, including EER, minDCF, and actDCF, which are critical for evaluating the effectiveness of anti-spoofing systems. However, the reliance on a single language (English) and the absence of real-world data in the testing phase could limit the generalizability of the findings.
Reproducibility
The authors have made their code and dataset publicly available, which is a positive aspect for reproducibility. However, the paper could benefit from more detailed descriptions of the experimental setup, including hyperparameter settings and model training specifics, to facilitate easier replication by other researchers.
Limitations
One limitation is the focus on English audio, which may restrict the applicability of the findings to other languages and dialects. Additionally, while the local attention mechanism shows promise, the paper acknowledges challenges in generalizing to unseen APIs, indicating that further research is needed to improve model robustness in this area.
Broader impact
The implications of this research are significant, as it addresses a critical security concern in the growing field of synthetic speech generation. By improving anti-spoofing detection methods, the work contributes to the development of more secure voice authentication systems, which are increasingly important in various applications, including finance, telecommunications, and personal security. The paper presents a comprehensive approach to enhancing speech anti-spoofing detection through the introduction of a diverse dataset and a novel model architecture. This work is a meaningful contribution to the field, addressing real-world challenges and setting a foundation for future research in audio security.
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