Fine-tuning Wav2vec for Vocal-burst Emotion Recognition
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Abstract (arXiv)
The ACII Affective Vocal Bursts (A-VB) competition introduces a new topic in affective computing, which is understanding emotional expression using the non-verbal sound of humans. We are familiar with emotion recognition via verbal vocal or facial expression. However, the vocal bursts such as laughs, cries, and signs, are not exploited even though they are very informative for behavior analysis. The A-VB competition comprises four tasks that explore non-verbal information in different spaces. This technical report describes the method and the result of SclabCNU Team for the tasks of the challenge. We achieved promising results compared to the baseline model provided by the organizers.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 1 Oct 2022.
Builds on
8 of 15 references.
References · 8
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
- 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 501
- 2020MLS: A Large-Scale Multilingual Dataset for Speech Researchcited by 476
- 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 467
- 2021Fine-tuning wav2vec2 for speaker recognitioncited by 39
- 2018End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learningcited by 12
- 2022The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expressioncited by 7
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