mlsanityData to 7 Oct 2026

An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysis

arXiv 2209.13914 ↗First posted 28 Sep 2022cs.SD cs.LG eess.AScited by 1

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Abstract (arXiv)

Affective speech analysis is an ongoing topic of research. A relatively new problem in this field is the analysis of vocal bursts, which are nonverbal vocalisations such as laughs or sighs. Current state-of-the-art approaches to address affective vocal burst analysis are mostly based on wav2vec2 or HuBERT features. In this paper, we investigate the use of the wav2vec successor data2vec in combination with a multitask learning pipeline to tackle different analysis problems at once. To assess the performance of our efficient multitask learning architecture, we participate in the 2022 ACII Affective Vocal Burst Challenge, showing that our approach substantially outperforms the baseline established there in three different subtasks.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 28 Sep 2022.

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15 of 22 references.

References · 15

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  2. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  3. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
  4. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  5. 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 718
  6. 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 462
  7. 2021Emotion Recognition from Speech Using Wav2vec 2.0 Embeddingscited by 158
  8. 2022Dawn of the transformer era in speech emotion recognition: closing the valence gapcited by 155
  9. 2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
  10. 2021Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognitioncited by 47
  11. 2022The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Burstscited by 16
  12. 2022The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expressioncited by 7
3 more
  1. 2022Comparing supervised and self-supervised embedding for ExVo Multi-Task learning trackcited by 4
  2. 2022Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion, Age, and Origin from Vocal Burstscited by 3
  3. 2022Self-supervision and Learnable STRFs for Age, Emotion, and Country Predictioncited by 2

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  1. 2025Semantic Matters: Multimodal Features for Affective Analysiscited by 5