We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65 based on errorful, automatically recognized words and prosody, and 71 based on word transcripts, compared to a chance baseline accuracy of 35 and human accuracy of 84 ) and a small reduction in word recognition error.
Previous research on DA modeling has generally focused on task-oriented dialogue, with three tasks in particular garnering much of the research effort. The Map Task corpus @cite_49 @cite_12 consists of conversations between two speakers with slightly different maps of an imaginary territory. Their task is to help one speaker reproduce a route drawn only on the other speaker's map, all without being able to see each other's maps. Of the DA modeling algorithms described below, TaylorEtAl:LS98 and Wright:98 were based on Map Task. The VERBMOBIL corpus consists of two-party scheduling dialogues. A number of the DA modeling algorithms described below were developed for VERBMOBIL, including those of MastEtAl:96 , WarnkeEtAl:97 , Reithinger:96 , Reithinger:97 , and Samuel:98 . The ATR Conference corpus is a subset of a larger ATR Dialogue database consisting of simulated dialogues between a secretary and a questioner at international conferences. Researchers using this corpus include Nagata:92 , [1994] NagataMorimoto:93 , NagataMorimoto:94 and KitaEtAl:96 . Table shows the most commonly used versions of the tag sets from those three tasks.
@cite_12 @cite_49
1784630400 2118142207
The paper describes a resource for the study of spontaneous speech under stress, a corpus of 216 unscripted task-oriented dialogues conducted by normal Canadian adults in the course of a sleep deprivation experiment under 3 drug conditions. Speakers carried out the route-communication task in alternation with a battery of other tasks over a 6-day study which included a 60-hour sleepless period. Each speaker participated in 12 dialogues. The design permits comparisons within speakers for sleep deprivation (baseline, deprived, post-recovery), and between speakers for drug condition (placebo, d-amphetamine, Modafinil) and number of conversational partners encountered. Preliminary examination of dialogue length, task performance, and aspects of dialogue strategy indicate effects of all these variables. Effects of sleep-deprivation and drug condition are less severe than those found in simpler tasks. This paper describes a corpus of unscripted, task-oriented dialogues which has been designed, digitally recorded, and transcribed to support the study of spontaneous speech on many levels. The corpus uses the Map Task (Brown, Anderson, Yule, and Shillcock, 1983) in which speakers must collaborate verbally to reproduce on one participant's map a route printed on the other's. In all, the corpus includes four conversations from each of 64 young adults and manipulates the following variables: familiarity of speakers, eye contact between speakers, matching between landmarks on the participants' maps, opportunities for contrastive stress, and phonological characteristics of landmark names. The motivations for the design are set out and basic corpus statistics are presented.