Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion, Age, and Origin from Vocal Bursts

June 24, 2022 ยท Declared Dead ยท ๐Ÿ› arXiv.org

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Authors Atijit Anuchitanukul, Lucia Specia arXiv ID 2206.12469 Category cs.SD: Sound Cross-listed cs.CL, eess.AS Citations 6 Venue arXiv.org Last Checked 3 months ago
Abstract
We present Burst2Vec, our multi-task learning approach to predict emotion, age, and origin (i.e., native country/language) from vocal bursts. Burst2Vec utilises pre-trained speech representations to capture acoustic information from raw waveforms and incorporates the concept of model debiasing via adversarial training. Our models achieve a relative 30 % performance gain over baselines using pre-extracted features and score the highest amongst all participants in the ICML ExVo 2022 Multi-Task Challenge.
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