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This session aims to bring together the speech and audio research community to address emerging challenges in responsible generative technologies.\u003C/p>","\u003Cp style=\"text-align:justify;\">We welcome submissions that tackle critical issues in neural watermarking [5-7], controllability [8], fairness [9], explainability [10,11], and measurement [12,13]. Ensuring precise control is vital for nuanced speech, while fairness is essential to prevent harmful biases. Enhancing explainability builds trust, and robust measurement methodologies are indispensable for evaluating system quality. Each of these areas demands rigorous attention to ensure the ethical and beneficial deployment of speech, music, and audio generative technologies. The session will highlight technical innovations, evaluation methodologies, and frameworks that contribute to safer, fairer, and more trustworthy generative AI systems. It aims to foster broader discussions around standards, best practices, and accountability in real-world deployments of speech and audio generative technologies. Ultimately, this session seeks to establish collaborative pathways and guidelines that will shape the future of responsible speech and audio generative AI.\u003C/p>\u003Ch4 class=\"h4\" style=\"text-align:justify;\">Special Session Objectives\u003C/h4>\u003Cp style=\"text-align:justify;\">This session seeks to advance state-of-the-art research on responsible speech and audio generation, focusing on accountability, controllability, fairness, and interpretability. It aims to bridge communities working on generative modeling and responsible AI while promoting collaboration across machine learning, ethics, human-computer interaction, and speech technology. In doing so, the session will support the development of tools, benchmarks, and evaluation protocols, fostering transparency, robustness, and inclusiveness in generative systems for speech, singing voice, music, and general audio. By encouraging dialogue around risks, governance, and the societal impact of speech and audio generation technologies, this session hopes to lay the groundwork for a more responsible future in this fast-evolving field.\u003C/p>\u003Ch4 class=\"h4\" style=\"text-align:justify;\">Potential Topics\u003C/h4>\u003Col style=\"list-style-type:decimal;\">\u003Cli style=\"text-align:justify;\">Preventing Misuse and Ensuring Accountability in Speech and Audio Generation\u003Col>\u003Cli style=\"text-align:justify;\">Robustness of generative speech and audio models against adversarial or malicious inputs\u003C/li>\u003Cli style=\"text-align:justify;\">Robust watermarking techniques to resist degradation and detect intentional tampering\u003C/li>\u003Cli style=\"text-align:justify;\">Evaluation framework simulating degradation and manipulation in real-world applications\u003C/li>\u003C/ol>\u003C/li>\u003Cli style=\"text-align:justify;\">Ensuring Fairness, and Inclusion in Speech and Audio Generation\u003Col>\u003Cli style=\"text-align:justify;\">Bias detection, mitigation, and fairness-aware evaluation across demographic, linguistic, and content-based variation\u003C/li>\u003Cli style=\"text-align:justify;\">Generalization and transfer learning across language, accent, musical genre, and acoustic domains\u003C/li>\u003Cli style=\"text-align:justify;\">Inclusive dataset design across diverse speakers, singing styles, musical genres, and acoustic conditions\u003C/li>\u003C/ol>\u003C/li>\u003Cli style=\"text-align:justify;\">Enabling Controllability and Transparency in Speech and Audio Generation\u003Col>\u003Cli style=\"text-align:justify;\">Techniques for user-aligned controllable synthesis across speech, singing, music, and general audio domains\u003C/li>\u003Cli style=\"text-align:justify;\">Approaches for human-in-the-loop steering or refining generated speech or audio\u003C/li>\u003Cli style=\"text-align:justify;\">Techniques for explainable speech or audio generation\u003C/li>\u003C/ol>\u003C/li>\u003Cli style=\"text-align:justify;\">Evaluating and Benchmarking for Trustworthy Speech and Audio Generation\u003Col>\u003Cli style=\"text-align:justify;\">Objective and subjective evaluation of synthesis quality, consistency and reliability across diverse conditions\u003C/li>\u003Cli style=\"text-align:justify;\">Fair and transparent evaluation across speech, singing, music, and other audio generation tasks\u003C/li>\u003Cli style=\"text-align:justify;\">Mitigating harmful, biased, or misleading content through ethical and risk management strategies\u003C/li>\u003C/ol>\u003C/li>\u003C/ol>\u003Ch4 class=\"h4\" style=\"text-align:justify;\">Organizers\u003C/h4>\u003Cp style=\"text-align:justify;\">Nicholas Andrews (noa@cs.jhu.edu) is a Senior Research Scientist at the HLTCOE, Johns Hopkins University, USA, with a recent interest in questions around privacy, detecting machine-generated content, and controllable/personalized generation.\u003C/p>\u003Cp style=\"text-align:justify;\">Xuanjun Chen (d12942018@ntu.edu.tw) is a Ph.D. student at National Taiwan University, working with Prof. Hung-yi Lee. His research interests include speech processing, machine learning, generative AI, and deepfake detection. He has served as a reviewer for ACL, EMNLP, ICASSP, Interspeech and as a committee member for the Codec-SUPERB Challenge at SLT 2024.\u003C/p>\u003Cp style=\"text-align:justify;\">Sung-Feng Huang (sungfengh@nvidia.com) is a Research Scientist at NVIDIA Research Taiwan. His research focuses on generative AI for speech and audio, with expertise in speech recognition, synthesis, separation, and machine learning such as self-supervised and meta-learning. He received his Ph.D. from National Taiwan University.\u003C/p>\u003Cp style=\"text-align:justify;\">Hung-yi Lee (hungyilee@ntu.edu.tw) is a Professor in the Department of Electrical Engineering and Computer Science at National Taiwan University. His research focuses on reducing the need for annotated data in speech and language processing. He has organized several major workshops on self-supervised learning at NeurIPS, AAAI, ACL, ICASSP, INTERSPEECH, and served as program chair or editor for events such as ISCSLP 2022, MLSS 2021, INLG 2023, ACML 2025, and a JSTSP special issue.\u003C/p>\u003Cp style=\"text-align:justify;\">Yuanchao Li (yuanchao.li@ed.ac.uk) is a final-year PhD student working on spoken language processing at CSTR, University of Edinburgh. He has organized the Special Session on Responsible Speech Foundation Models at Interspeech 2024 and 2025, focusing on ethical and trustworthy speech technologies. He has also co-organized related workshops and challenges at ACM MM Asia and SLT.\u003C/p>\u003Cp style=\"text-align:justify;\">Hemlata Tak (hemlata.tak@pindrop.com) is a Research Scientist-II at Pindrop, USA. She received her Ph.D. degree from the Sorbonne University, France. She was also a co-organiser of the ASVspoof 5 and SASV 2022 challenges. Her research interests include voice biometrics, audio deepfake detection, and source tracing.\u003C/p>\u003Cp style=\"text-align:justify;\">Jennifer Williams (J.Williams@soton.ac.uk) is an Assistant Professor at the University of Southampton and a Scientific Advisor to the Alan Turing Institute BridgeAI programme. Her research focuses on trustworthy and explainable speech processing, including speaker recognition, voice privacy, and deepfake detection. She is a Senior IEEE Member and served as Chair of the ISCA SIG on Security and Privacy in Speech Communication (2023–2025).\u003C/p>\u003Cp style=\"text-align:justify;\">Haibin Wu (haibinwu@microsoft.com) is a Senior Applied Scientist at Microsoft. He received his Ph.D. from National Taiwan University. He has organized an internal challenge named Codec-SUPERB at SLT 2024. His research interests include speech foundation models, neural audio codecs, prompt engineering, and deepfake detection.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://codecfake.github.io/RespSA-GenAI/\">Visit Website\u003C/a>\u003C/p>","2025-04-25T15:19:45.180Z","2025-04-28T20:02:19.831Z","2025-04-25T15:19:48.644Z","83",[],"-47",{"id":48,"session":1249},{"id":48,"title":1250,"teaser":1251,"body":1252,"createdAt":1253,"updatedAt":1254,"publishedAt":1255,"url_path_id":1256,"contacts":1257,"url_path":1258},"SS2. Frontiers in Deepfake Voice Detection and Beyond","\u003Cp style=\"text-align:justify;\">With the rapid development of deep learning and generative AI, it has become increasingly easy to create or manipulate synthetic media. Even inexperienced users can now generate highly realistic content with minimal effort. While this progress brings exciting opportunities, it also introduces significant risks, particularly when these technologies are misused for malicious purposes such as spreading misinformation or compromising security systems.\u003C/p>","\u003Cp style=\"text-align:justify;\">This special session aims to address the emerging challenges posed by recent advances in deepfake audio generation, and to promote research beyond conventional anti-spoofing tasks. We seek to foster discussion and innovation in tackling frontiers in deepfake detection while ensuring robustness against existing threats and mitigating catastrophic forgetting. Specifically, we welcome contributions that explore novel detection techniques, evaluation protocols, and datasets related but not limited to:\u003C/p>\u003Cul>\u003Cli style=\"text-align:justify;\">Partial Spoof Detection, Localization, and Diarization\u003C/li>\u003Cli style=\"text-align:justify;\">Detection of Codec-Based Deepfake Speech (CodecFake)\u003C/li>\u003Cli style=\"text-align:justify;\">Proactive Defense\u003C/li>\u003Cli style=\"text-align:justify;\">Singing Voice Deepfake Detection\u003C/li>\u003Cli style=\"text-align:justify;\">Multimodal Deepfake Detection\u003C/li>\u003Cli style=\"text-align:justify;\">Adversarial Attack and Defenses\u003C/li>\u003Cli style=\"text-align:justify;\">Generalization and/or Domain Adaptation\u003C/li>\u003Cli style=\"text-align:justify;\">Source Tracing\u003C/li>\u003Cli style=\"text-align:justify;\">Human Perception vs. Machine Detection\u003C/li>\u003Cli style=\"text-align:justify;\">Analysis, Explainability, Reliability, and Evaluation on Deepfake Detection.\u003C/li>\u003Cli style=\"text-align:justify;\">Defenses Against Other Attacks (e.g., Shallow Fakes, Fake Emotion, etc.)\u003C/li>\u003C/ul>\u003Ch4 class=\"h4\" style=\"text-align:justify;\">Organizers\u003C/h4>\u003Cp style=\"text-align:justify;\">Dr. Lin Zhang (zlin@ieee.org) is a postdoctoral fellow at Johns Hopkins University, USA. She proposed the first publicly accessible PartialSpoof database, and defined tasks in the Partial Spoof scenario. She is also a co-author of CodecFake+.\u003C/p>\u003Cp style=\"text-align:justify;\">Mr. You Zhang (you.zhang@rochester.edu) is a final-year PhD candidate in the Department of Electrical and Computer Engineering at the University of Rochester, USA. He also organized the first Singing Voice Deepfake Detection Challenge at SLT 2024.\u003C/p>\u003Cp style=\"text-align:justify;\">Dr. Haibin Wu (f07921092@ntu.edu.tw) is a senior applied scientist at Microsoft, USA. He is the proposer of CodecFake and CodecFake+, which aim to detect deepfake audio generated by codec-based speech generation systems.\u003C/p>\u003Cp style=\"text-align:justify;\">Prof. Xin Wang (wangxin@nii.ac.jp) is a project associate professor at NII, Japan. He is a co-author of PartialSpoof and an organizer of ASVspoof and VoicePrivacy.\u003C/p>\u003Cp style=\"text-align:justify;\">Dr. Hemlata Tak (hemlata.tak@pindrop.com) is a research scientist at Pindrop, USA. She is one of the organizers of Source Tracing special session at interspeech2025, the ASVspoof 5 and the SASV 2022 challenge.\u003C/p>\u003Cp style=\"text-align:justify;\">Dr. Erica Cooper (ecooper@nict.go.jp) is a senior researcher at the National Institute of Information and Communications Technology, Japan. She is a co-author of PartialSpoof and one of the organizers of the VoiceMOS challenge series.\u003C/p>\u003Cp style=\"text-align:justify;\">Prof. Nicholas Evans (evans@eurecom.fr) is a Professor at EURECOM, France. He is a co-author of PartialSpoof, and one of the organizers of ASVspoof and VoicePrivacy.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://sites.google.com/view/deepfake-voice\">Visit Website\u003C/a>\u003C/p>","2025-04-25T15:25:30.579Z","2025-04-25T16:19:45.104Z","2025-04-25T15:25:40.567Z","84",[],"-48",{"data":1260,"meta":1261},{"id":141,"heading":574,"createdAt":579,"updatedAt":580,"publishedAt":13,"url_path_id":581,"url_path":575,"contentType":95},{},1778851857674]