Human-human task-oriented conversations corpus for interaction quality modeling

Speech is the main modality for human communication. It can tell a lot about its owner: their emotions, intelligence, age, psychological portrait and others properties. Such information can be useful in different fields: in call centres for improvement in the quality of service, in designing Spoken Dialogue Systems for better adaptation of a system to users' behaviour, in the automatization of some processes for analysing people's psychological state in a situation with a high level of responsibility, for example, in a space programme. One such characteristic is the Interaction Quality. The Interaction Quality is a quality metric, which is used in the field of Spoken Dialogue Systems to evaluate the quality of human-computer interaction. As well as in Spoken Dialogue Systems, the Interaction Quality can be applied for estimating the quality of human-human conversations. As with any investigation in the field of speech analytics, for modelling the Interaction Quality for human-human conversations a specific corpus of task-oriented dialogues is required. Although there is a large number of speech corpora, for some tasks, as, for example, for Interaction Quality modelling, it is still difficult to find appropriate specific corpora. That is why we decided to generate our own corpus based on dialogues between the customers and agents of one company. In this paper we describe the current state of this corpus. It contains 53 dialogues, corresponding to 1165 exchanges. It includes audio features, paralinguistic information and experts' labels. We plan to extend this corpus both in the feature set and in the observations.