使用置信度分数和强化学习修正语音情感描述子的可解释且可信的语音情感识别
Explainable and Trustworthy Speech Emotion Recognition Using Confidence Score and Reinforcement Learning Rectified Speech Emotion Descriptors
AI总结：
提出基于置信度分数和强化学习的在线语音情感描述子修正方法，用于后训练语音情感识别系统，在IEMOCAP和MELD上分别取得2.9%和3.3%的绝对性能提升。
摘要：
可解释且可信的语音情感识别（SER）至今仍是一项具有挑战性的任务，这主要是由于缺乏带有可靠语音情感描述子（SED）标签（如韵律特征和说话人特征）的SER数据。本文提出了一种基于置信度分数和强化学习（RL）的在线SED修正方法，用于在自动标注的SED标签上对SER系统进行后训练。在IEMOCAP和MELD上的实验表明，结合所提出的置信度分数和基于RL的SED修正方法的可解释SER系统，在性能上始终优于没有数据选择或SED修正的基线系统。最佳系统集成了这两个组件，在IEMOCAP和MELD基准测试上，分别比没有数据选择和SED修正的基线系统高出2.9%和3.3%的绝对SER增益（相对增益分别为3.7%和5.4%）。
英文摘要：
Explainable and trustworthy speech emotion recognition (SER) remains a challenging task to date, largely due to the scarcity of SER data with reliable speech emotion descriptor (SED) labels, such as prosodic features and speaker traits. This paper presents a confidence score and reinforcement learning (RL) based on-the-fly SED rectification approach for post-training SER systems on automatically annotated SED labels. Experiments on IEMOCAP and MELD suggest that explainable SER systems incorporating the proposed confidence score and RL-based SED rectification approach consistently outperform baselines without data selection or SED rectification. The best performing system, which integrates both components, surpasses the baseline without data selection and SED rectification, achieving SER gains of 2.9% and 3.3% absolute (3.7% and 5.4% relative) on IEMOCAP and MELD benchmarks, respectively.
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。
