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

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arxiv 2206.12469 v2 pith:UL6NOOI4 submitted 2022-06-24 cs.SD cs.CLeess.AS

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

classification cs.SD cs.CLeess.AS
keywords burst2vecmulti-taskadversarialapproachburstsemotionoriginvocal
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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