用对话上下文和大模型提升语音情感识别准确率
Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs
- 先清洗转录文本，再按对话片段构建上下文
- 最佳方案比基线高20%准确率，位居挑战赛榜首
- 适合研究语音情感分析与大模型应用的学者
语音情感识别（SER）旨在从口语中识别情绪状态。2024年IEEE SLT-GenSEC挑战赛聚焦于后自动语音识别（ASR）阶段的情感识别任务，鼓励参赛者探索大语言模型（LLMs）在仅使用文本数据下的情感识别能力。本文提出一种新方法：首先对所有可用转录文本进行清洗以确保数据可靠性；然后将完整对话分割为更小的对话片段，并利用这些片段作为上下文来预测目标语句的情绪；最后系统研究了不同上下文长度与提示技术对预测精度的影响。最佳提交方案在未加权准确率上较基线提升20%，取得挑战赛最佳成绩。所有实验代码、预测结果及日志文件均已公开。
原文摘要 · Abstract (English)
Speech Emotion Recognition (SER) focuses on identifying emotional states from spoken language. The 2024 IEEE SLT-GenSEC Challenge on Post Automatic Speech Recognition (ASR) Emotion Recognition tasks participants to explore the capabilities of large language models (LLMs) for emotion recognition using only text data. We propose a novel approach that first refines all available transcriptions to ensure data reliability. We then segment each complete conversation into smaller dialogues and use these dialogues as context to predict the emotion of the target utterance within the dialogue. Finally, we investigated different context lengths and prompting techniques to improve prediction accuracy. Our best submission exceeded the baseline by 20% in unweighted accuracy, achieving the best performance in the challenge. All our experiments' codes, prediction results, and log files are publicly available.
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