User Expertise Modeling and Adaptivity in a Speech-Based E-Mail System

User Expertise Modelling and Adaptivity in a Speech-based E-mail System Kristiina JOKINEN University of Helsinki and University of Art and Design Helsinki Hämeentie 135C 00560 Helsinki kjokinen@uiah.fi Kari KANTO University of Art and Design Helsinki Hämeentie 135C 00560 Helsinki kanto@uiah.fi Abstract This paper describes the user expertise model in AthosMail, a mobile, speech-based e-mail system. The model encodes the system’s assumptions about the user expertise, and gives recommendations on how the system should respond depending on the assumed competence levels of the user. The recommendations are realized as three types of explicitness in the system responses. The system monitors the user’s competence with the help of parameters that describe e.g. the success of the user’s interaction with the system. The model consists of an online and an offline version, the former taking care of the expertise level changes during the same session, the latter modelling the overall user expertise as a function of time and repeated interactions. 1 Introduction Adaptive functionality in spoken dialogue systems is usually geared towards dealing with communication disfluencies and facilitating more natural interaction (e.g. Danieli and Gerbino, 1995; Litman and Pan, 1999; Krahmer et al, 1999; Walker et al, 2000). In the AthosMail system (Turunen et al., 2004), the focus has been on adaptivity that addresses the user’s expertise levels with respect to a dialogue system’s functionality, and allows adaptation to take place both online and between the sessions. The main idea is that while novice users need guidance, it would be inefficient and annoying for experienced users to be forced to listen to the same instructions every time they use the system. For instance, already (Smith, 1993) observed that it is safer for beginners to be closely guided by the system, while experienced users like to take the initiative which results in more efficient dialogues in terms of decreased average completion time and a decreased average number of utterances. However, being able to decide when to switch from guiding a novice to facilitating an expert requires the system to be able to keep track of the user's expertise level. Depending on the system, the migration from one end of the expertise scale to the other may take anything from one session to an extended period of time. In some systems (e.g. Chu-Carroll, 2000), user inexperience is countered with initiative shifts towards the system, so that in the extreme case, the system leads the user from one task state to the next. This is a natural direction if the application includes tasks that can be pictured as a sequence of choices, like choosing turns from a road map when navigating towards a particular place. Examples of such a task structure include travel reservation systems, where the requested information can be given when all the relevant parameters have been collected. If, on the other hand, the task structure is flat, system initiative may not be very useful, since nothing is gained by leading the user along paths that are only one or two steps long. Yankelovich …

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