# MultiAPI Spoof: A Multi-API Dataset and Local-Attention Network for Speech Anti-spoofing Detection

arXiv: https://arxiv.org/abs/2512.07352 · HTML: https://mlsanity.com/paper/2512.07352/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2025.

## Built on by (most cited first, 2 of 2)

- [CALM: Class-Conditional Sparse Attention Vectors for Large Audio-Language Models](https://mlsanity.com/paper/2602.07077/) (2026)
- [PC-Mix: Partial-Component Audio Spoofing Detection under Mixed Speech and Environmental Sound Conditions](https://mlsanity.com/paper/2607.10345/) (2026)

## Abstract (arXiv)

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. Furthermore, we propose Nes2Net-LA, a local-attention enhanced variant of Nes2Net that improves local context modeling and fine-grained spoofing feature extraction. Based on this dataset, we also define the API tracing task, enabling fine-grained attribution of spoofed audio to its generation source. Experiments show that Nes2Net-LA achieves state-of-the-art performance and offers superior robustness, particularly under diverse and unseen spoofing conditions. Code footnotehttps://github.com/XuepingZhang/MultiAPI-Spoof and dataset footnotehttps://xuepingzhang.github.io/MultiAPI-Spoof-Dataset/ have been released.