Recognition of emotion in a realistic dialogue scenario

Data is used from a so-called Wizard of Oz scenario to get more realistic data instead of simulated anger, and the classi cation rate for the two classes "emotion" and "neutral" is signi cantly worse for these more realism data.

Abstract

Nowadays modern automatic dialogue systems are able to understand complex sentences instead of only a few commands like Stop or No.In a call-center, such a system should be able to determine in a critical phase of the dialogue if the call should be passed over to a human operator.Such a critical phase can be indicated by the customer's vocal expression.Other studies prooved that it is possible to distinguish between anger and neutral speech w i t h prosodic features alone.Subjects in these studies were mostly people acting or simulating emotions like anger.In this paper we use data from a so-called Wizard of O z (WoZ) scenario to get more realistic data instead of simulated anger.As shown below, the classi cation rate for the two classes "emotion" (class E) and "neutral" (class :E) is signi cantly worse for these more realistic data.Furthermore the classi cation results are heavily speaker dependent.Prosody alone might t h us not be su cient and has to be supplemented by the use of other knowledge sources such as the detection of repetitions, reformulations, swear words, and dialogue acts.