tailieunhanh - Báo cáo khoa học: "Turn-Taking Cues in a Human Tutoring Corpus"

Most spoken dialogue systems are still lacking in their ability to accurately model the complex process that is human turntaking. This research analyzes a humanhuman tutoring corpus in order to identify prosodic turn-taking cues, with the hopes that they can be used by intelligent tutoring systems to predict student turn boundaries. Results show that while there was variation between subjects, three features were significant turn-yielding cues overall. In addition, a positive relationship between the number of cues present and the probability of a turn yield was demonstrated. . | Turn-Taking Cues in a Human Tutoring Corpus Heather Friedberg Department of Computer Science University of Pittsburgh Pittsburgh Pa 15260 UsA friedberg@ Abstract Most spoken dialogue systems are still lacking in their ability to accurately model the complex process that is human turntaking. This research analyzes a humanhuman tutoring corpus in order to identify prosodic turn-taking cues with the hopes that they can be used by intelligent tutoring systems to predict student turn boundaries. Results show that while there was variation between subjects three features were significant turn-yielding cues overall. In addition a positive relationship between the number of cues present and the probability of a turn yield was demonstrated. 1 Introduction Human conversation is a seemingly simple everyday phenomenon that requires a complex mental process of turn-taking in which participants manage to yield and hold the floor with little pause inbetween speaking turns. Most linguists subscribe to the idea that this process is governed by a subconscious internal mechanism that is a set of cues or rules that steers humans toward proper turntaking Duncan 1972 . These cues may include lexical features such as the words used to end the turn or prosodic features such as speaking rate pitch and intensity Cutler and Pearson 1986 . While successful turn-taking is fairly easy for humans to accomplish it is still difficult for models to be implemented in spoken dialogue systems. Many systems use a set time-out to decide 94 when a user is finished speaking often resulting in unnaturally long pauses or awkward overlaps Ward et. al. 2005 . Others detect when a user interrupts the system known as barge-in though this is characteristic of failed turn-taking rather than successful conversation Glass 1999 . Improper turn-taking can often be a source of user discomfort and dissatisfaction with a spoken dialogue system. Little work has been done to study turn-taking in tutoring so we