# Bridging Attribution and Open-Set Detection using Graph-Augmented Instance Learning in Synthetic Speech

arXiv: https://arxiv.org/abs/2601.07064 · HTML: https://mlsanity.com/paper/2601.07064/

## Key facts

- Cited by 1 papers in the map (data through 20 Oct 2026).
- Placed in Anti-spoofing and adversarial audio attacks, one of 11 directions in Speech and audio language models.
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 11 Jan 2026.

## Placed in

- Speech and audio language models / [Anti-spoofing and adversarial audio attacks](https://mlsanity.com/speech/anti-spoofing-and-adversarial-audio-attacks/)

## Builds on (most cited first, 17 of 17)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings](https://mlsanity.com/paper/1910.10838/) (2019)
- [UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training](https://mlsanity.com/paper/2110.05752/) (2021)
- [Audio Deepfake Detection with Self-Supervised WavLM and Multi-Fusion Attentive Classifier](https://mlsanity.com/paper/2312.08089/) (2023)
- [Audio Mamba: Selective State Spaces for Self-Supervised Audio Representations](https://mlsanity.com/paper/2406.02178/) (2024)
- [HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methods](https://mlsanity.com/paper/2309.08208/) (2023)
- [TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes](https://mlsanity.com/paper/2506.05802/) (2025)
- [DiffSSD: A Diffusion-Based Dataset For Speech Forensics](https://mlsanity.com/paper/2409.13049/) (2024)
- [Towards Source Attribution of Singing Voice Deepfake with Multimodal Foundation Models](https://mlsanity.com/paper/2506.03364/) (2025)
- [Self-Attention and Hybrid Features for Replay and Deep-Fake Audio Detection](https://mlsanity.com/paper/2401.05614/) (2024)

## Built on by (most cited first, 1 of 1)

- [Bridging the SEA Gap: An Initial Benchmark for Neural Audio Codec-Synthesized Speech Deepfakes in South-East Asian Languages](https://mlsanity.com/paper/2606.15968/) (2026)

## Abstract (arXiv)

We propose a unified framework for not only attributing synthetic speech to its source but also for detecting speech generated by synthesizers that were not encountered during training. This requires methods that move beyond simple detection to support both detailed forensic analysis and open-set generalization. To address this, we introduce SIGNAL, a hybrid framework that combines speech foundation models (SFMs) with graph-based modeling and open-set-aware inference. Our framework integrates Graph Neural Networks (GNNs) and a k-Nearest Neighbor (KNN) classifier, allowing it to capture meaningful relationships between utterances and recognize speech that doesn`t belong to any known generator. It constructs a query-conditioned graph over generator class prototypes, enabling the GNN to reason over relationships among candidate generators, while the KNN branch supports open-set detection via confidence-based thresholding. We evaluate SIGNAL using the DiffSSD dataset, which offers a diverse mix of real speech and synthetic audio from both open-source and commercial diffusion-based TTS systems. To further assess generalization, we also test on the SingFake benchmark. Our results show that SIGNAL consistently improves performance across both tasks, with Mamba-based embeddings delivering especially strong results. To the best of our knowledge, this is the first study to unify graph-based learning and open-set detection for tracing synthetic speech back to its origin.