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Dialogue Act Detection from Human-Human Spoken Conversations

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 67 - Number 5
Year of Publication: 2013
Nithin Ramacandran

Nithin Ramacandran. Article: Dialogue Act Detection from Human-Human Spoken Conversations. International Journal of Computer Applications 67(5):24-27, April 2013. Full text available. BibTeX

	author = {Nithin Ramacandran},
	title = {Article: Dialogue Act Detection from Human-Human Spoken Conversations},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {67},
	number = {5},
	pages = {24-27},
	month = {April},
	note = {Full text available}


Accurate detection of dialogue acts is essential for understanding human conversations and to recognize emotions. This requires 1) the segmentation of human-human dialogs into turns, 2) the intra-turn segmentation into DA boundaries and 3) the classification of each segment according to a DA tag. Most dialogue act classification models approaches the problem of identifying the different DA segments within an utterance in separate fashion: first, DA boundary segmentation within an utterance was addressed with generative or discriminative approaches then, DA labels were assigned to such boundaries based on multi-classification. This paper, presents an effective approach to improve the accuracy of dialogue act recognition from speech signal by combining acoustic and linguistic features. This paper adopts the use of a silence removal algorithm based on Mahalanobis Distance for the segmentation of human-human dialogs into turns and proposes the keyword spotting feature to reduce the ambiguity of opinion vs. non-opinion statements and agreements vs. acknowledgements, occurs while classifying the dialogue acts.


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