INTERSPEECH 2004 - ICSLP
This paper reports on our experience in the automatic detection of dialog acts in human-human spoken dialog corpora. Two hypotheses underlie this work: first, word position is important in identifying the dialog act; and second, there is a strong grammar constraining the sequence of dialog acts. A Memory Based Learning approach has been used to detect dialog acts. Experiments are carried out with a known number of utterances per speaker turn, and with a hypothesized number of utterances determined using a language model for automatic utterance boundary detection. In order to verify our first hypothesis, the model trained on a French corpus was tested on an English corpus for a similar task and on a French corpus from a different domain. A correct dialog act detection rate of 83% is obtained for the same domain and language conditions and about 75% for the cross-language and cross-domain conditions.
Bibliographic reference. Rosset, Sophie / Lamel, Lori (2004): "Automatic detection of dialog acts based on multilevel information", In INTERSPEECH-2004, 309-312.