David Combei
I work on explainable audio deepfake detection — building detectors that hold up on real-world audio and can show why a recording is flagged as fake.
About
Making audio deepfake detectors trustworthy
I am a PhD student in Machine Learning at the Artificial Intelligence Research Institute (AIRi@UTCN), Technical University of Cluj-Napoca. My research focuses on explainable audio deepfake detection: understanding what speech anti-spoofing models actually learn, and making their decisions interpretable to the people who rely on them.
I contribute to several research projects, and my work aims to bridge the gap between robust ML models and real-world audio challenges — from in-the-wild deepfakes to source attribution of synthetic speech.
I am working under the supervision of Adriana Stan at the Technical University of Cluj-Napoca.
Research
Collaborations & projects
Publications
Selected papers
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2025
On the Contribution of Lexical Features to Speech Emotion Recognition
Proc. of the 13th Conference on Speech Technology and Human-Computer Dialogue (SpeD)
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2025
Unmasking real-world audio deepfakes: A data centric approach
Interspeech 2025
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2025
TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
Interspeech 2025
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2024
WavLM model ensemble for audio deepfake detection
ASVspoof 2024 Workshop (Automatic Speaker Verification and Spoofing Countermeasures), pp. 170–175
Events
Events & presentations
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2025
Eastern European Machine Learning Summer School (EEML)
Selected to attend the highly competitive EEML summer school in Sarajevo.
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2025
Interspeech 2025 — Rotterdam, the Netherlands
Presented “Unmasking real-world audio deepfakes: A data centric approach” and “TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes”.
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2024
Romanian AI Days 2024
One of the students selected to present their BSc thesis, “Text and Speech Emotion Recognition using pre-trained self-supervised models”.
Google Scholar
LinkedIn
Hugging Face


