标题： 面向可解释的情感识别：使用机器学习识别关键特征 显示英文标题

标题： Towards interpretable emotion recognition: Identifying key features with machine learning

摘要： 无监督方法，如wav2vec2和HuBERT，在音频任务中取得了最先进的性能，导致研究重点从可解释特征转向其他方向。 然而，这些方法缺乏可解释性，限制了它们在医学等关键领域中的应用，因为在这些领域中理解特征的相关性至关重要。 为了更好地理解无监督模型的特征，识别与给定任务相关的可解释特征仍然至关重要。 在本工作中，我们专注于情感识别，并使用机器学习算法来识别和推广与此任务最重要的可解释特征。 虽然以前的研究已经探索了情感识别中的特征相关性，但它们通常受到狭窄情境的限制，并且结果不一致。 我们的方法旨在克服这些局限性，为识别最重要的可解释特征提供一个更广泛、更稳健的框架。 显示英文摘要

摘要： Unsupervised methods, such as wav2vec2 and HuBERT, have achieved state-of-the-art performance in audio tasks, leading to a shift away from research on interpretable features. However, the lack of interpretability in these methods limits their applicability in critical domains like medicine, where understanding feature relevance is crucial. To better understand the features of unsupervised models, it remains critical to identify the interpretable features relevant to a given task. In this work, we focus on emotion recognition and use machine learning algorithms to identify and generalize the most important interpretable features for this task. While previous studies have explored feature relevance in emotion recognition, they are often constrained by narrow contexts and present inconsistent findings. Our approach aims to overcome these limitations, providing a broader and more robust framework for identifying the most important interpretable features.