SER Evals：面向语音情感识别的域内与域外基准测试
SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition
AI总结：
该研究提出名为SER Evals的大规模多语言基准，通过logit调整应对类别分布差异，系统评估语音情感识别模型的域内/域外泛化能力，发现Whisper模型跨语言表现优于专用自监督模型，为SER领域后续研究提供重要基准资源。
摘要：
随着强大的自监督学习（SSL）模型的出现，语音情感识别（SER）已取得显著进展。然而，这些模型对不同语言和情感表达的泛化能力仍是一大挑战。我们提出了一个大规模基准，用于评估最先进的SER模型在域内和域外设置下的鲁棒性与适应性。我们的基准包含多样化的多语言数据集，重点关注较不常用的语料库，以评估模型对新数据的泛化能力。我们采用logit调整（对数似然调整）来应对不同的类别分布，并构建了单一数据集集群以开展系统性评估。令人意外的是，我们发现主要为自动语音识别设计的Whisper模型，在跨语言SER任务中表现优于专用的SSL模型。我们的结果凸显了研发更鲁棒、泛化性更强的SER模型的必要性，而我们的基准也将成为推动该领域未来研究的宝贵资源。
英文摘要：
Speech emotion recognition (SER) has made significant strides with the advent of powerful self-supervised learning (SSL) models. However, the generalization of these models to diverse languages and emotional expressions remains a challenge. We propose a large-scale benchmark to evaluate the robustness and adaptability of state-of-the-art SER models in both in-domain and out-of-domain settings. Our benchmark includes a diverse set of multilingual datasets, focusing on less commonly used corpora to assess generalization to new data. We employ logit adjustment to account for varying class distributions and establish a single dataset cluster for systematic evaluation. Surprisingly, we find that the Whisper model, primarily designed for automatic speech recognition, outperforms dedicated SSL models in cross-lingual SER. Our results highlight the need for more robust and generalizable SER models, and our benchmark serves as a valuable resource to drive future research in this direction.
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