Abstract
Emotion Recognition in Conversation has been seen to be widely applicable in call center analytics, opinion mining, finance, retail, healthcare, and other industries. In a call center scenario, the role of the call center agent is not just confined to receiving calls but to also provide good customer experience by pacifying the frustration or anger of the customers. This can be achieved by maintaining neutral and positive emotion from the agent. As in any conversation, the emotion of one speaker is usually dependent on the emotion of other speaker. Hence the positive emotion of an agent, accompanied with the right resolution will help in enhancing customer experience. This can change an unhappy customer to a happy one. Imparting the right resolution at right time becomes easier if the agent has the insight of the emotion of future utterances. To predict the emotions of the future utterances we propose a novel architecture, Emotion Recognition and Forecasting in Conversation. Our proposed ERFC architecture considers multi modalities, different attributes of emotion, context and the interdependencies of the utterances of the speakers in the conversation. Our intensive experiments on the IEMOCAP dataset have shown the feasibility of the proposed ERFC. This approach can provide a tremendous business value for the applications like call center, where the happiness of customer is utmost important.
Abstract (translated)
情感识别在对话中的应用范围广泛，适用于呼叫中心分析、意见挖掘、金融、零售、医疗保健等行业。在一个呼叫中心的情境中，座席人员的角色不仅仅是接听电话，还需要通过缓解客户的挫败感或愤怒来提供良好的客户体验。这可以通过保持中立和积极的情感状态来实现。在任何对话中，一名说话者的情绪通常都会受到另一名说话者情绪的影响。因此，当座席执行了正确的解决方案时，伴随其的积极情感有助于提升顾客体验，从而将不满意的顾客转变为满意顾客。如果座席人员能够预见未来的发言情绪，则可以在正确的时间提供适当的解决方案。为了预测未来发言的情感，我们提出了一种新颖的架构——对话中的情感识别和预测（ERFC）。我们的提出的ERFC架构考虑了多模态、不同的情绪属性、上下文以及对话中说话者言论之间的相互依赖关系。我们在IEMOCAP数据集上进行的密集实验已经证明了所提议的ERFC方法的可行性。这种方法可以为像呼叫中心这样的应用提供巨大的商业价值，因为在这种情境下，顾客的满意度至关重要。
URL
https://arxiv.org/abs/2509.18175
