通过多模态与多任务学习，提升自然语音情感识别准确率。
Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Prediction
- 融合文本嵌入与性别预测，增强模型对情感的感知能力。
- 在MSP-Podcast数据集上取得挑战赛第一、二名，性能领先。
- 适合关注真实场景下情感分析的研究者与工程师。
自然语境下的语音情感识别（SER）对语音处理领域构成重大挑战，主要问题包括标注者间意见分歧及数据分布不均。本文提出一个可复现的框架，在语音情感识别自然语境挑战赛（IS25-SER Challenge）-任务2中取得顶级（第一）表现，评估基于MSP-Podcast数据集。系统通过多模态学习、多任务学习及不平衡数据处理策略应对上述挑战。最佳模型通过引入文本嵌入、预测性别，并将‘其他’（O）和‘无共识’（X）样本纳入训练集实现。该系统在挑战赛中斩获第一、第二名，最高性能由简单两系统集成达成。
原文摘要 · Abstract (English)
Speech emotion recognition (SER) in naturalistic conditions presents a significant challenge for the speech processing community. Challenges include disagreement in labeling among annotators and imbalanced data distributions. This paper presents a reproducible framework that achieves superior (top 1) performance in the Emotion Recognition in Naturalistic Conditions Challenge (IS25-SER Challenge) - Task 2, evaluated on the MSP-Podcast dataset. Our system is designed to tackle the aforementioned challenges through multimodal learning, multi-task learning, and imbalanced data handling. Specifically, our best system is trained by adding text embeddings, predicting gender, and including ``Other'' (O) and ``No Agreement'' (X) samples in the training set. Our system's results secured both first and second places in the IS25-SER Challenge, and the top performance was achieved by a simple two-system ensemble.
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