mlsanityData to 7 Oct 2026

The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts

arXiv 2205.01780 ↗First posted 3 May 2022eess.AS cs.LG cs.SDcited by 16

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Abstract (arXiv)

The ICML Expressive Vocalization (ExVo) Competition is focused on understanding and generating vocal bursts: laughs, gasps, cries, and other non-verbal vocalizations that are central to emotional expression and communication. ExVo 2022, includes three competition tracks using a large-scale dataset of 59,201 vocalizations from 1,702 speakers. The first, ExVo-MultiTask, requires participants to train a multi-task model to recognize expressed emotions and demographic traits from vocal bursts. The second, ExVo-Generate, requires participants to train a generative model that produces vocal bursts conveying ten different emotions. The third, ExVo-FewShot, requires participants to leverage few-shot learning incorporating speaker identity to train a model for the recognition of 10 emotions conveyed by vocal bursts. This paper describes the three tracks and provides performance measures for baseline models using state-of-the-art machine learning strategies. The baseline for each track is as follows, for ExVo-MultiTask, a combined score, computing the harmonic mean of Concordance Correlation Coefficient (CCC), Unweighted Average Recall (UAR), and inverted Mean Absolute Error (MAE) (SMTL) is at best, 0.335 SMTL; for ExVo-Generate, we report Fr'echet inception distance (FID) scores ranging from 4.81 to 8.27 (depending on the emotion) between the training set and generated samples. We then combine the inverted FID with perceptual ratings of the generated samples (SGen) and obtain 0.174 SGen; and for ExVo-FewShot, a mean CCC of 0.444 is obtained.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 3 May 2022.

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14 of 32 references.

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2 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 11; 2023: 2; 2024: 2; 2025: 0; 2026: 1.

16 papers.

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  1. 2022An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Eracited by 32
  2. 2022The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expressioncited by 7
  3. 2022Exploring speaker enrolment for few-shot personalisation in emotional vocalisation predictioncited by 3
  4. 2022Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion, Age, and Origin from Vocal Burstscited by 3
  5. 2022Generating Diverse Vocal Bursts with StyleGAN2 and MEL-Spectrogramscited by 3
  6. 2024Expressivity and Speech Synthesiscited by 3
  7. 2022Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expressioncited by 2
  8. 2022Self-supervision and Learnable STRFs for Age, Emotion, and Country Predictioncited by 2
  9. 2022Synthesizing Personalized Non-speech Vocalization from Discrete Speech Representationscited by 2
  10. 2022Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformerscited by 1
  11. 2022Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Burstscited by 1
  12. 2022An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysiscited by 1
4 more
  1. 2023EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologiescited by 1
  2. 2023A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognitioncited by 1
  3. 2026The 2026 ACII Dyadic Conversations (DaiKon) Workshop & Challengecited by 1
  4. 2024The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Datacited by 0