无需训练，通过统计更新提升语音情感识别在分布外场景的性能
EMO-TTA: Improving Test-Time Adaptation of Audio-Language Models for Speech Emotion Recognition
- 利用期望最大化算法动态更新类别条件统计量，实现测试时分布估计
- 在6个分布外基准上均超越现有方法，平均提升超过3个百分点
- 轻量无参设计，适合实时部署，尤其适用于资源受限场景
基于音频-语言模型（ALMs）的语音情感识别（SER）在测试时面对分布偏移仍易退化。测试时自适应（TTA）虽有潜力，但常依赖梯度更新或提示调优，灵活性不足。本文提出Emo-TTA，一种轻量、无需训练的自适应框架，通过期望最大化过程对类条件统计量进行增量更新，以模型预测为先验，实现显式的测试时分布估计。该方法仅作用于单个测试样本，不修改模型权重。在六个分布外SER基准上的实验表明，相比现有TTA基线，本方法实现了持续的准确率提升，验证了统计自适应在对齐模型输出与动态测试分布方面的有效性。
原文摘要 · Abstract (English)
Speech emotion recognition (SER) with audio-language models (ALMs) remains vulnerable to distribution shifts at test time, leading to performance degradation in out-of-domain scenarios. Test-time adaptation (TTA) provides a promising solution but often relies on gradient-based updates or prompt tuning, limiting flexibility and practicality. We propose Emo-TTA, a lightweight, training-free adaptation framework that incrementally updates class-conditional statistics via an Expectation-Maximization procedure for explicit test-time distribution estimation, using ALM predictions as priors. Emo-TTA operates on individual test samples without modifying model weights. Experiments on six out-of-domain SER benchmarks show consistent accuracy improvements over prior TTA baselines, demonstrating the effectiveness of statistical adaptation in aligning model predictions with evolving test distributions.
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