# Towards Robust Speech Deepfake Detection via Human-Inspired Reasoning

arXiv: https://arxiv.org/abs/2603.10725 · HTML: https://mlsanity.com/paper/2603.10725/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Placed in

- LLM reasoning / [Audio-language model reasoning](https://mlsanity.com/lr/audio-language-model-reasoning/)

## Builds on (most cited first, 28 of 28)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Qwen2-Audio Technical Report](https://mlsanity.com/paper/2407.10759/) (2024)
- [SALMONN: Towards Generic Hearing Abilities for Large Language Models](https://mlsanity.com/paper/2310.13289/) (2023)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs](https://mlsanity.com/paper/2506.14245/) (2025)
- [Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models](https://mlsanity.com/paper/2507.08128/) (2025)
- [FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset](https://mlsanity.com/paper/2108.05080/) (2021)
- [XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model](https://mlsanity.com/paper/2406.04904/) (2024)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale](https://mlsanity.com/paper/2408.08739/) (2024)
- [WaveFake: A Data Set to Facilitate Audio Deepfake Detection](https://mlsanity.com/paper/2111.02813/) (2021)
- [ADD 2023: the Second Audio Deepfake Detection Challenge](https://mlsanity.com/paper/2305.13774/) (2023)
- [SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based on Speech and Audio Information](https://mlsanity.com/paper/2505.13237/) (2025)
- [Step-Audio-R1 Technical Report](https://mlsanity.com/paper/2511.15848/) (2025)
- [SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation](https://mlsanity.com/paper/2510.14664/) (2025)
- [Golos: Russian Dataset for Speech Research](https://mlsanity.com/paper/2106.10161/) (2021)
- [AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge](https://mlsanity.com/paper/2408.17352/) (2024)
- [Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models](https://mlsanity.com/paper/2506.01683/) (2025)
- [Generalizable Audio Deepfake Detection via Hierarchical Structure Learning and Feature Whitening in Poincar'e sphere](https://mlsanity.com/paper/2508.01897/) (2025)
- [Benchmarking Time-localized Explanations for Audio Classification Models](https://mlsanity.com/paper/2506.04391/) (2025)
- [HoliAntiSpoof: Audio LLM for Holistic Speech Anti-Spoofing](https://mlsanity.com/paper/2602.04535/) (2026)
- [SARA: Stress Test Reasoning in Audio Deepfake Detection](https://mlsanity.com/paper/2601.03615/) (2026)

## Built on by (most cited first, 4 of 4)

- [How to Leverage Synthetic Speech for LLM-Based ASR Systems?](https://mlsanity.com/paper/2606.29031/) (2026)
- [Large Audio Language Models for Spoofing-Aware Speaker Verification](https://mlsanity.com/paper/2607.14753/) (2026)
- [SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision](https://mlsanity.com/paper/2609.39679/) (2026)
- [SEAR: Spoofing Evidence-Grounded Audio Reasoning Benchmark for Audio Language Models](https://mlsanity.com/paper/2609.39847/) (2026)

## Abstract (arXiv)

The modern generative audio models can be used by an adversary in an unlawful manner, specifically, to impersonate other people to gain access to private information. To mitigate this issue, speech deepfake detection (SDD) methods started to evolve. Unfortunately, current SDD methods generally suffer from the lack of generalization to new audio domains and generators. More than that, they lack interpretability, especially human-like reasoning that would naturally explain the attribution of a given audio to the bona fide or spoof class and provide human-perceptible cues. In this paper, we propose HIR-SDD, a novel SDD framework that combines the strengths of Large Audio Language Models (LALMs) with the chain-of-thought reasoning derived from the novel proposed human-annotated dataset. Experimental evaluation demonstrates both the effectiveness of the proposed method and its ability to provide reasonable justifications for predictions.