The 1st Workshop on Speech Emotion Analysis in the Era of LLMs (SEAL) focuses on advancing computational methods for recognizing, interpreting, and generating emotions in speech using modern speech-processing architectures, self-supervised speech models, multimodal systems, and Speech Language Models. The workshop is motivated by the growing need for machines to understand not only the linguistic content of speech but also the speaker’s emotional intent, which is essential for applications such as expressive text-to-speech synthesis, speech-to-speech translation, conversational agents, healthcare technologies, education, and human–computer interaction. SEAL aims to address persistent challenges involving multilingual and low-resource speech, cultural and speaker variability, spontaneous and code-mixed communication, background noise, class imbalance, subjective annotation, and speaker-independent emotion representation. It will bring together researchers from computer science, engineering, linguistics, psychology, cognitive science, artificial intelligence, and related disciplines to present and discuss original research on speech emotion analysis and emotion-aware intelligent speech technologies.
Submit original and unpublished research relevant to the workshop themes.
Follow the prescribed submission format and guidelines.
Submit the paper through the official submission portal before the deadline.
All submissions will undergo peer review.
Accepted papers must be registered and presented at the workshop.
Submission dates, registration details, and presentation instructions will be announced later.
Paper Submission Deadline: October 18, 2026
Notification of Acceptance: November 15, 2026
Camera-Ready Submission: November 25, 2026
Author Registration Deadline: November 27, 2026
Workshop Date: December 20, 2026
Assistant Professor, Dept. of Computer Science & Engineering, Jadavpur University, Kolkata
Professor, Dept. of Computer Science & Engineering, Jadavpur University, Kolkata
Professor, Maynooth University, Ireland