Design and Development of a Human-Machine Dialog Corpus for the Automated Assessment of Conversational English Proficiency

This paper presents a carefully designed corpus of scored spoken conversations between English language learners and a dialog system to facilitate research and development of both human and machine scoring of dialog interactions. We collected speech, demographic and user experience data from non-native speakers of English who interacted with a virtual boss as part of a workplace pragmatics skill building application. Expert raters then scored the dialogs on a custom rubric encompassing 12 aspects of conversational proficiency as well as an overall holistic performance score. We analyze key corpus statistics and discuss the advantages of such a corpus for both human and machine scoring.