RI-Small: Collaborative Research: Dispatcher's Assistant for Emergency First Response

在危机反应领域，情绪表现非常复杂，极端情绪很常见。虽然语音技术多年来取得了重大进展，但在嘈杂环境中识别和理解情感语音仍然是一个巨大的挑战。考虑到碎片化和不符合语法的话语，再加上自动语音识别（ASR）的错误，理解这种语言是令人生畏的。此外，对情感检测和语言理解之间的关系的分析研究很少，而情感检测和语言理解传统上被认为是并行的独立任务。甚至当其中一个任务的输出被用作另一个任务的输入特征时，通常，在训练期间使用“真实”分类而不是“估计”分类（如果系统在实际设置中使用，就会出现这种情况），导致不匹配和性能下降。本项目试图克服当前方法的局限性，重点分析情感和意图之间的依赖程度，并通过多任务学习来研究语言理解和情感检测任务的联合分类方法。该项目的主要智力价值是对开发端到端信息处理系统的综合研究，该系统有可能对危机响应过程产生重大影响。对于语音处理而言，个人与紧急调度人员之间的通信以及响应期间响应者之间的通信构成了很大的挑战，因为呼叫者通常非常情绪化，所使用的语言反映了这一点。处理这样的语音需要大量的研究，而这个项目可能是朝着这一类型迈出的第一步。

In crisis response domains, emotional manifestations are very complex and extreme emotions are common. While speech technologies have shown significant progress over the years, recognizing and understanding emotional speech in noisy environments is still a big challenge. Understanding such language is daunting given fragmented and ungrammatical utterances in addition to errorsfrom automatic speech recognition (ASR). Furthermore, there is littleresearch on the analysis of the relationship between emotion detection and language understanding which have traditionally been viewed as parallel independent tasks. Even when the output of one of these tasks is used as an input feature to the other, typically, during training a "true" classification is used instead of the "estimated" classes (as would be the case if the system were to be used in a real-setting) resulting in a mismatch and degraded performance. This project attempts to overcome such a limitation of current approaches by focusing on analyzing the degree of the dependencies between emotion and intent and investigating joint classification methods via multitask learning for language understanding and emotion detection tasks.The primary intellectual merit of the project is integrated research on developing an end-to-end information processing system that has the potential to very significantly impact the crisis response process. For speech processing, communucation between individuals and emergency dispatch personnel as well as communications during responders during response pose a big challenge since callers are typically very emotional and the language used reflects that. Processing such speech requires significant research, and this project can is a first step toward this genre.