Linguistic Adaptations in Spoken Human-Computer Dialogues Empirical Studies of User Behavior

This thesis addresses the question of how speakers adapttheir language when they interact with a spoken dialoguesystem. In human–human dialogue, people continuously adaptto their conversational partners at different levels. Wheninteracting with computers, speakers also to some extent adapttheir language to meet (what they believe to be) theconstraints of the dialogue system. Furthermore, if a problemoccurs in the human–computer dialogue, patterns oflinguistic adaptation are often accentuated.In this thesis, we used an empirical approach in which aseries of corpora of human–computer interaction werecollected and analyzed. The systems used for data collectionincluded both fully functional stand-alone systems in publicsettings, and simulated systems in controlled laboratoryenvironments. All of the systems featured animated talkingagents, and encouraged users to interact using unrestrictedspontaneous language. Linguistic adaptation in the corpora wasexamined at the phonetic, prosodic, lexical, syntactic andpragmatic levels.Knowledge about users’linguistic adaptations can beuseful in the development of spoken dialogue systems. If we areable to adequately describe their patterns of occurrence (atthe different linguistic levels at which they occur), we willbe able to build more precise user models, thus improvingsystem performance. Our knowledge of linguistic adaptations canbe useful in at least two ways: first, it has been shown thatlinguistic adaptations can be used to identify (andsubsequently repair) errors in human–computer dialogue.Second, we can try to subtly influence users to behave in acertain way, for instance by implicitly encouraging a speakingstyle that improves speech recognition performance.