论文标题
BUST2VEC：一种可预测情绪，年龄和来源的对抗性多任务方法
Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion, Age, and Origin from Vocal Bursts
论文作者
论文摘要
我们介绍了我们的多任务学习方法，以预测人声爆发中的情感，年龄和起源（即祖国/语言）。 BUST2VEC利用预先训练的语音表示来捕获原始波形的声学信息，并通过对抗训练结合了模型的概念。我们的模型使用预摘要的功能获得了基准比基线的相对30％的性能增长，并在ICML EXVO 2022多任务挑战中所有参与者中获得最高分。
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.
