Call for Papers

Secure, safe, and privacy-preserving speech and audio AI

We invite original work on risks, evaluations, defenses, and deployment practices for trustworthy speech and audio models. We also welcome already accepted papers for workshop presentation through our non-archival track.

Topics of Interest

• Digital adversarial attacks on speech and audio models, including ASR, TTS, and speaker recognition.
• Physical-world and real-world audio attacks, including over-the-air and replay attacks.
• Robustness, safety, and reliability evaluation of audio-language and multimodal models, benchmarks, and protocols.
• Deepfake and synthetic voice misuse, including voice cloning and impersonation.
• Hallucinations, misalignment, and other unintended behaviors in audio and multimodal generative models.
• Data poisoning and backdoor attacks across the model lifecycle, including data, training, fine-tuning, and release.
• Privacy risks and privacy-preserving training or deployment for speech and audio systems.
• Model extraction and IP attacks, plus provenance and attribution for synthetic audio.
• Defensive methods and system-level safeguards for speech, audio, and multimodal systems.
• Safety and robustness in multilingual, multicultural, and underrepresented languages.
• Safety evaluation, red-teaming, and alignment of LLMs, audio-language models, and multimodal foundation models.
• Prompt injection, jailbreaks, instruction-following failures, and unsafe tool use in LLMs and multimodal frameworks.
• Uncertainty, calibration, refusal behavior, and responsible generation across language, speech, audio, and multimodal systems.