Dec
04
Date: 04-December-2026
Time: 11:00 AM ET (New York Time)
Presenter: Dr. Jee-weon Jung
Based on the IEEE Xplore® article under the same title
Published IEEE Open Journal of Signal Processing, January 2025.
Download article: Original article is open access and publicly available for download. ARTICLE LINK
About the topic:
This talk presents our presenters team’s work across two papers—Text-to-Speech in the Wild and SpoofCeleb. Current speech deepfake detection systems are mostly trained on datasets containing clean speech recorded in controlled environments, along with synthesized speech generated by TTS models also trained on clean data. However, real-world deployment conditions are diverse and noisy, creating a mismatch. TTS model’s dependence on clean training data has long limited the creation of realistic in-the-wild anti-spoofing datasets.
In this talk, our presenter will describe how they tackle this challenge and built a large-scale in-the-wild dataset for training and evaluating robust speech deepfake detection systems.
About the presenter:
Dr. Jee-weon Jung
Jee-weon Jung (M’ 18) received the B.S. and the Ph.D. degrees in computer science & engineering from the University of Seoul, Korea, in 2017 and 2021, respectively,
He is currently a senior research scientist at Apple and was a research scientist at Naver corporation and a Postdoctoral Research Associate at Carnegie Mellon University, USA. He is an organizer of ASVspoof, VoxSRC, SASV, and WildSpoof series. His research focuses on audio speaker recognition, speech deepfake detection, and other speech-related tasks.
