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Annotation and Detection of Emotion in a Task-oriented Human-Human Dialog Corpus

Automatic emotion detection is potentially important for customer care in the context of call center services. A major difficulty is that the expression of emotion is quite complex in the context of agent-client dialogs. Due to unstated rules of politeness it is common to have shaded emotions, in which the interlocutors attempt to limit their internal attitude (frustration, anger, disappointment). Furthermore, the telephonic quality speech complicates the tasks of detecting perceptual cues and of extracting relevant measures from the signal. Detecting real emotions can be helpful to follow the evolution of the human-computer dialogs, enabling dynamic modification of the dialog strategies and influencing the final outcome. This paper reports on three studies: the first concerns the use of multi-level annotations including emotion tags for diagnosis of the dialog state; the second investigates automatic emotion detection using linguistic information; and the third reports on two perceptual tests for identifying emotions as well as the prosodic and textual cues which signal them. Two experimental conditions were considered – with and without the capability of listening the audio signal. Finally, we propose a new set of emotions/attitudes tags suggested by the previous experiments and discuss perspectives.