ITR: Recognizing and Understanding Emotion in Speech

口语不仅仅是听得见的文本。 我们产生单词和短语的方式告诉我们的听众很多关于我们的精神状态和意图，我们的话和行动。 虽然机器在识别当前口语对话系统中的句子方面变得越来越熟练，但它们在检测说话者是否沮丧或自信，或者他们是否试图欺骗或向听众传达有用信息等方面仍然非常差。 尽管在识别行为、实验室言语中的情感和意图的语言和非语言线索方面的工作很有前途，而且早期的结果在更自然的环境中识别了有限类型的情感，但我们对可靠的语言线索只有有限的理解（声学，韵律，词法和句法）这些现象，因此不知道如何自动识别的现象自动。PI将首先进行一系列的实验室实验，以确定声学，韵律，词汇和句法线索的情绪和意图，在引发（非行动）的讲话。 他们将同时发现新的功能，这些功能可能有助于自动识别情感和意图，如欺骗性，信心和挫折/愤怒，使用具有增强标签的可用语料库，并为这些说话者状态/意图标记新语料库。 他们随后将在实验室记录上测试这些特征，并通过分析这些记录来确定可能提出的新特征。 结果应该提供一个更好的理解，什么样的听觉线索表征某些扬声器状态/意图，这些提供可靠的功能，他们的自动identifiation.From从实用的角度来看，自动识别扬声器状态/意图应该是相当大的好处交互式语音响应系统，如呼叫中心响应系统，在电话银行或旅游预订系统，或教程系统。 说话者状态/意图的自动识别还应该为当前依赖于说话者状态/意图的人类评估的各种应用中的说话者筛选提供有用的信息。

Spoken language is much more than simply audible text. The way we produce words and phrases tells our hearers much about our mental state and the intentions that underlie our words and actions. While machines have become increasingly proficient at recognizing sentences in current spoken dialog systems, they are still very poor at detecting, inter alia, whether speakers are frustrated or confident, or whether they are trying to deceive or to convey helpful information to their hearers. Despite promising work in identifying verbal and non-verbal cues to emotion and intention in acted, laboratory speech, and early results identifying limited types of emotion in more natural settings, we have only a limited understanding of reliable verbal cues (acoustic, prosodic, lexical and syntactic) to these phenomena and consequently do not know how to automatically recognize the phenomena automatically.The PIs will first conduct a series of laboratory experiments to identify acoustic, prosodic, lexical and syntactic cues to emotion and intention in elicited (non-acted) speech. They will, in parallel, discover new features that may be useful in automatically identifying emotions and intentions such as deceptiveness, confidence, and frustration/anger using available corpora with augmented labeling and labeling new corpora for these speaker states/intentions. They will subsequently test these features on the laboratory recordings and identify new features that may be suggested by analysis of these recordings. The result should provide a better understanding of what auditory cues characterize certain speaker states/intentions and which of these provide reliable features for their automatic identification.From a practical point of view, identifying speaker state/intentions automatically should be of considerable benefit for interactive voice response systems such as call center response systems, over the phone banking or travel reservation systems, or tutorial systems. Automatic identification of speaker state/intentions should also provide useful information for speaker screening in a variety of applications that currently depend upon human assessment of speaker state/intention.