Nov
20
Date: 20-November-2025
Time: 09:30 AM ET (New York Time)
Presenter: Dr. Lin Zhang
Based on the IEEE Xplore® article titled: “The Partial Spoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance”
Published: IEEE/ACM Transactions on Audio, Speech, and Language Processing, December 2022.
Download article: Original article is open access and publicly available for download. ARTICLE LINK
Abstract
Speech can easily be manipulated through techniques, such as text-to-speech synthesis, voice conversion, replay, tampering, adversarial attacks, and more. However, when the manipulation is applied only to a minor portion of an audio, the remaining real segments can have a dominant influence upon human listeners and make machine detection extremely challenging. Therefore, there is an urgent need to explore such a scenario, where synthetic speech is embedded within otherwise real audio. The primary objective of this webinar is to review research efforts aimed at defending against such partially fake audio. The presenter will focus on relevant databases, explainable analyses, and three core tasks: (1) Spoof Detection: Whether the utterance is spoofed? This task aligns with the common task in the spoofing community distinguishing whether an audio is real or fake. (2) Spoof Localization: When do spoofs happen? This task aims to determine the location of spoof segments within audio. (3) Spoof Diarization: What attacks when? This task not only locates the spoofed segments but also discriminates the specific spoofing techniques employed.
Biography
Lin Zhang (M’20) received the M.S. degree in software engineering from Tianjin University, Tianjin, China, in 2020, and the Ph.D. degree in computer science from the Graduate University for Advanced Studies / National Institute of Informatics, Tokyo, Japan.
She is currently a Postdoctoral Fellow at the Center for Language and Speech Processing, Johns Hopkins University, USA. She has also visited and/or worked at Brno University of Technology and Duke Kunshan University. Her research interests include speech security and privacy, as well as machine learning.
