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ICASSP
2011
IEEE

Simultaneous dialog act segmentation and classification from human-human spoken conversations

8 years 7 months ago
Simultaneous dialog act segmentation and classification from human-human spoken conversations
An accurate identification dialog acts (DAs), which represent the illocutionary aspect of communication, is essential to support the understanding of human conversations. 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. This process is particularly challenging when both segmentation and tagging are automated and utterance hypotheses derive from the erroneous results of ASR. In this paper, we use Conditional Random Fields to learn models for simultaneous segmentation and labeling of DAs from whole human-human spoken dialogs. We identify the best performing lexical feature combinations on the LUNA and SWITCHBOARD human-human dialog corpora and compare performances to those of discriminative D classifiers based on manually segmented utterances. Additionally, we assess our models’ robustness to recognition errors, showing that DA identification is ro...
Silvia Quarteroni, Alexei V. Ivanov, Giuseppe Ricc
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Silvia Quarteroni, Alexei V. Ivanov, Giuseppe Riccardi
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