Speech act, an essential element of conversation, underlies the principle that an utterance in a dialogue is an action being performed by a speaker. Since speech acts do convey speakers’ intentions and opinions, it is key for the computer to identify and verify the speech act of a user’s utterance in a spoken dialogue system. This chapter presents a few approaches to speech act identification and verification in Chinese spoken dialogue systems. Approaches using ontology-based partial pattern trees and semantic dependency graphs (SDGs) for speech act modeling are described. A verification mechanism using a latent semantic analysis (LSA) based Bayesian belief model (BBM) is adopted to improve the performance of speech act identification. Experimental results show the SDG-based approach outperforms the Bayes’ classifier and the ontology-based partial pattern trees. By integrating discourse analysis into the SDG-based approach, the results show improvements obtained not only in the speech act identification accuracy rate, but also in the performance of semantic object extraction. Furthermore, LSA-based BBM for speech act verification further improves the performance of speech act identification.
All Science Journal Classification (ASJC) codes
- Computer Science(all)