5 citations · 12 across the 12 of their papers we have counts for
11 papers · 1 filter
Singlish, Can or Not? Fine-Tuning and Evaluating Zero-Shot TTS for Singapore English
Ivan Kukanov, Zheng Xin Chai
Zero-shot text-to-speech (ZS-TTS) achieves near-human quality for standard English, but it copies regional accents poorly. Prompted with a short Singlish utterance, state-of-the-ar…
How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection
Ivan Kukanov, Janne Laakkonen, Ville Hautamäki
Meta-learning for domain generalization (MLDG) improves out-of-distribution speech deepfake detection over empirical risk minimization (ERM) when both objectives train low-rank ada…
RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations
Hieu-Thi Luong, Xuechen Liu, Ivan Kukanov +2
RADAR Challenge 2026 is an APSIPA Grand Challenge on Robust Audio Deepfake Recognition under Media Transformations, designed to simulate realistic media conditions in real-world au…
Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection
Janne Laakkonen, Ivan Kukanov, Ville Hautamäki
Foundation models such as Wav2Vec2 excel at representation learning in speech tasks, including audio deepfake detection. However, after being fine-tuned on a fixed set of bonafide…
KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features
Ivan Kukanov, Jun Wah Ng
The rapid development of audio-driven talking head generators and advanced Text-To-Speech (TTS) models has led to more sophisticated temporal deepfakes. These advances highlight th…
Generalizable speech deepfake detection via meta-learned LoRA
Janne Laakkonen, Ivan Kukanov, Ville Hautamäki
Reliable detection of speech deepfakes (spoofs) must remain effective when the distribution of spoofing attacks shifts. We frame the task as domain generalization and show that ins…