基于颜色的情绪表示用于语音情绪识别
Color-based Emotion Representation for Speech Emotion Recognition
AI总结：
本文提出利用颜色属性表示情绪，通过构建回归模型和多任务学习提升语音情绪识别的性能。
摘要：
语音情绪识别（SER）传统上依赖于分类或维度标签。然而，这种技术在表示情绪的多样性和可解释性方面存在局限。为克服这一限制，我们专注于颜色属性，如色调、饱和度和价值，将情绪表示为连续且可解释的评分。我们通过众包标注了一个情感语音语料库，并分析了这些属性。此外，我们利用机器学习和深度学习构建了SER中颜色属性的回归模型，并探索了颜色属性回归与情绪分类的多任务学习。结果表明，我们展示了语音中颜色属性与情绪之间的关系，并成功开发了用于SER的颜色属性回归模型。我们还展示了多任务学习提高了每个任务的性能。
英文摘要：
Speech emotion recognition (SER) has traditionally relied on categorical or dimensional labels. However, this technique is limited in representing both the diversity and interpretability of emotions. To overcome this limitation, we focus on color attributes, such as hue, saturation, and value, to represent emotions as continuous and interpretable scores. We annotated an emotional speech corpus with color attributes via crowdsourcing and analyzed them. Moreover, we built regression models for color attributes in SER using machine learning and deep learning, and explored the multitask learning of color attribute regression and emotion classification. As a result, we demonstrated the relationship between color attributes and emotions in speech, and successfully developed color attribute regression models for SER. We also showed that multitask learning improved the performance of each task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
