# Easy, Interpretable, Effective: openSMILE for voice deepfake detection

arXiv: https://arxiv.org/abs/2408.15775 · HTML: https://mlsanity.com/paper/2408.15775/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 30 of 31)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech](https://mlsanity.com/paper/2105.06337/) (2021)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search](https://mlsanity.com/paper/2005.11129/) (2020)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://mlsanity.com/paper/2006.06873/) (2020)
- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild](https://mlsanity.com/paper/2210.02437/) (2022)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion](https://mlsanity.com/paper/2107.10394/) (2021)
- [Audio Deepfake Detection: A Survey](https://mlsanity.com/paper/2308.14970/) (2023)
- [Investigating self-supervised front ends for speech spoofing countermeasures](https://mlsanity.com/paper/2111.07725/) (2021)
- [Speech is Silver, Silence is Golden: What do ASVspoof-trained Models Really Learn?](https://mlsanity.com/paper/2106.12914/) (2021)
- [The Vicomtech Audio Deepfake Detection System based on Wav2Vec2 for the 2022 ADD Challenge](https://mlsanity.com/paper/2203.01573/) (2022)
- [Improved DeepFake Detection Using Whisper Features](https://mlsanity.com/paper/2306.01428/) (2023)
- [Audio Deepfake Detection with Self-Supervised WavLM and Multi-Fusion Attentive Classifier](https://mlsanity.com/paper/2312.08089/) (2023)
- [Parrotron: An End-to-End Speech-to-Speech Conversion Model and its Applications to Hearing-Impaired Speech and Speech Separation](https://mlsanity.com/paper/1904.04169/) (2019)
- [A robust audio deepfake detection system via multi-view feature](https://mlsanity.com/paper/2403.01960/) (2024)
- [Low-Resource Multilingual and Zero-Shot Multispeaker TTS](https://mlsanity.com/paper/2210.12223/) (2022)
- [Source Tracing of Audio Deepfake Systems](https://mlsanity.com/paper/2407.08016/) (2024)
- [An explainability study of the constant Q cepstral coefficient spoofing countermeasure for automatic speaker verification](https://mlsanity.com/paper/2004.06422/) (2020)
- [Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features](https://mlsanity.com/paper/2208.01214/) (2022)
- [Attacker Attribution of Audio Deepfakes](https://mlsanity.com/paper/2203.15563/) (2022)
- [Open Challenges in Synthetic Speech Detection](https://mlsanity.com/paper/2209.07180/) (2022)

## Built on by (most cited first, 5 of 5)

- [Survey on AI-Generated Media Detection: From Non-MLLM to MLLM](https://mlsanity.com/paper/2502.05240/) (2025)
- [Automated Dysphagia Screening Using Noninvasive Neck Acoustic Sensing](https://mlsanity.com/paper/2602.02725/) (2026)
- [Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment](https://mlsanity.com/paper/2606.21306/) (2026)
- [What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection](https://mlsanity.com/paper/2607.09891/) (2026)
- [CRAF: Cross-View Residual-Aware Fusion for Deepfake Speech Detection](https://mlsanity.com/paper/2609.13842/) (2026)

## Abstract (arXiv)

In this paper, we demonstrate that attacks in the latest ASVspoof5 dataset -- a de facto standard in the field of voice authenticity and deepfake detection -- can be identified with surprising accuracy using a small subset of very simplistic features. These are derived from the openSMILE library, and are scalar-valued, easy to compute, and human interpretable. For example, attack A10`s unvoiced segments have a mean length of 0.09 +- 0.02, while bona fide instances have a mean length of 0.18 +- 0.07. Using this feature alone, a threshold classifier achieves an Equal Error Rate (EER) of 10.3% for attack A10. Similarly, across all attacks, we achieve up to 0.8% EER, with an overall EER of 15.7 +- 6.0%. We explore the generalization capabilities of these features and find that some of them transfer effectively between attacks, primarily when the attacks originate from similar Text-to-Speech (TTS) architectures. This finding may indicate that voice anti-spoofing is, in part, a problem of identifying and remembering signatures or fingerprints of individual TTS systems. This allows to better understand anti-spoofing models and their challenges in real-world application.