The ICML Expressive Vocalizations (ExVo) Workshop and Competition 2022 introduces, for the first time in a competition setting, the machine learning problem of understanding and generating vocal bursts – a wide range of emotional non-linguistic utterances. Participants of ExVo are presented with three tasks that utilize a single dataset. The dataset and three tasks draw attention to new innovations in emotion science and capture 10 dimensions of emotion reliably perceived in distinct vocal bursts: Awe, Excitement, Amusement, Awkwardness, Fear, Horror, Distress, Triumph, Sadness and Surprise. Of particular interest to the ICML community, these tasks highlight the need for advanced machine learning techniques for multi-task learning, audio generation, and personalized few-shot learning of nonverbal expressive style.

With studies of vocal emotional expression often relying on significantly smaller datasets insufficient to apply the latest machine learning innovations, the ExVo competition and workshop provides an unprecedented platform for the development and discussion of novel strategies for understanding vocal bursts and will enable unique forms of collaborations by leading researchers from diverse disciplines. Organized by leading researchers in emotion science and machine learning, the following three tasks are proposed: the Multi-task High-Dimensional Emotion, Age & Country Task (ExVo Multi-Task); the Generative Emotional Vocal Burst Task (ExVo Generate); and the Few-Shot Emotion Recognition task (ExVo Few-Shot).

Important dates (AoE)
- Challenge Opening (data available): April 1, 2022.
- Baselines and paper released: April 8, 2022.
- ExVo MultiTask submission deadline: May 12, 2022.
- ExVo Few-Shot (test-labels): May 13, 2022.
- Workshop paper submission: ~~May 20, 2022~~ Extended June 6 2022.

For those interested in submitting research to the ExVo workshop outside of the competition, we encourage contributions covering the following topics:
- Detecting and Understanding Vocal Emotional Behavior
- Multi-Task Learning in Affective Computing
- Generating Nonverbal Vocalizations or Speech Prosody
- Personalized Machine Learning for Affective Computing
- Other topics related to Affective Verbal and Nonverbal Vocalization