Joint Segmentation and Classification of Dialog Acts using Conditional Random Fields

This paper investigates the use of conditional random fields for joint segmentation and classification of dialog acts exploiting both word and prosodic features that are directly available from a speech recognizer. To validate the approach experiments are conducted with two different sets of dialog act types under both reference and speech to text conditions. Although the proposed framework is conceptually simpler than previous attempts at segmentation and classification of DAs it outperforms all previous systems for a task based on the ICSI (MRDA) meeting corpus.