“我们关心”：改进客户服务对话中语码混合语音情感识别
"We care": Improving Code Mixed Speech Emotion Recognition in Customer-Care Conversations
AI总结：
本文针对客户服务对话中语码混合语音情感识别问题，构建自然语音情感数据集NSED，并通过引入词级VAD值将负向情感识别准确率提升2%，有助于开发更具同理心的对话智能体。
摘要：
语音情感识别（SER）是识别口语话语中所表达情感的任务。情感识别对于在法律、医疗、教育和客户支持等领域构建稳健的对话智能体至关重要。大多数已发表的SER研究使用的数据集是在无噪声环境中由专业演员参与创建的。在客户服务对话等自然场景中，音频通常充满噪声，说话者会根据需要经常在不同语言之间切换。我们与对话式AI领域的一家领先独角兽公司合作，开发了自然语音情感数据集（NSED）。NSED是一个自然的语码混合语音情感数据集，其中对话中的每个话语都标注了情感、情感倾向、效价、唤醒度和支配度（VAD）值。在本文中，我们展示了通过引入词级VAD值，我们在NSED的负向情感识别任务上比基线值提高了2%。负向情感识别的高准确率至关重要，因为表达负面意见/看法的客户需要被紧急安抚，以免投诉和不满情绪滚雪球般扩大并失控。负面意见的快速升级对商业利益至关重要。因此，我们的研究可用于开发在此类情境下更加礼貌和富有同理心的对话智能体。
英文摘要：
Speech Emotion Recognition (SER) is the task of identifying the emotion expressed in a spoken utterance. Emotion recognition is essential in building robust conversational agents in domains such as law, healthcare, education, and customer support. Most of the studies published on SER use datasets created by employing professional actors in a noise-free environment. In natural settings such as a customer care conversation, the audio is often noisy with speakers regularly switching between different languages as they see fit. We have worked in collaboration with a leading unicorn in the Conversational AI sector to develop Natural Speech Emotion Dataset (NSED). NSED is a natural code-mixed speech emotion dataset where each utterance in a conversation is annotated with emotion, sentiment, valence, arousal, and dominance (VAD) values. In this paper, we show that by incorporating word-level VAD value we improve on the task of SER by 2%, for negative emotions, over the baseline value for NSED. High accuracy for negative emotion recognition is essential because customers expressing negative opinions/views need to be pacified with urgency, lest complaints and dissatisfaction snowball and get out of hand. Escalation of negative opinions speedily is crucial for business interests. Our study then can be utilized to develop conversational agents which are more polite and empathetic in such situations.
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。
