Dialog Act Modeling for Conversational Speech (1998)
| Venue: | IN AAAI SPRING SYMPOSIUM ON APPLYING MACHINE LEARNING TO DISCOURSE PROCESSING |
| Citations: | 26 - 4 self |
BibTeX
@INPROCEEDINGS{Stolcke98dialogact,
author = {Andreas Stolcke and Elizabeth Shriberg and Rebecca Bates and Noah Coccaro and Daniel Jurafsky and Rachel Martin and Marie Meteer and Klaus Ries and Paul Taylor and Carol Van Ess-Dykema},
title = {Dialog Act Modeling for Conversational Speech},
booktitle = {IN AAAI SPRING SYMPOSIUM ON APPLYING MACHINE LEARNING TO DISCOURSE PROCESSING},
year = {1998},
pages = {98--105},
publisher = {AAAI Press}
}
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Abstract
We describe an integrated approach for statistical modeling of discourse structure for natural conversational speech. Our model is based on 42 `dialog acts' (e.g., Statement, Question, Backchannel, Agreement, Disagreement, Apology), which were hand-labeled in 1155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We developed several models and algorithms to automatically detect dialog acts from transcribed or automatically recognized words and from prosodic properties of the speech signal, and by using a statistical discourse grammar. All of these components were probabilistic in nature and estimated from data, employing a variety of techniques (hidden Markov models, N-gram language models, maximum entropy estimation, decision tree classifiers, and neural networks). In preliminary studies, we achieved a dialog act labeling accuracy of 65% based on recognized words and prosody, and an accuracy of 72% based on word transcripts. Since humans achiev...
