Introduction to artificial intelligence and expert systems
Introduction to artificial intelligence and expert systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Algorithms for bigram and trigram word clustering
Speech Communication
Grammar fragment acquisition using syntactic and semantic clustering
Speech Communication
Structuring utterance records of requirements elicitation meetings based on speech act theory
ICRE '96 Proceedings of the 2nd International Conference on Requirements Engineering (ICRE '96)
Template-driven generation of prosodic information for Chinese concatenative synthesis
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Transfer-based statistical translation of Taiwanese sign language using PCFG
ACM Transactions on Asian Language Information Processing (TALIP)
Ontology-based speech act identification in a bilingual dialog system using partial pattern trees
Journal of the American Society for Information Science and Technology
Combining lexical, syntactic and prosodic cues for improved online dialog act tagging
Computer Speech and Language
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In a spoken dialog system, it is an important problem for the computer to identify the speech act (SA) from a user's utterance due to the variability of spoken language. In this paper, a corpus-based fuzzy fragment-class Markov model (FFCMM) is proposed to model the syntactic characteristics of a speech act and used to choose the speech act candidates. A speech act verification process, that estimates the conditional probability of a speech act given a sequence of fragments, is used to verify the speech act candidate. Most main design procedures are statistical- and corpus-based to reduce manual work. In order to evaluate the proposed method, a spoken dialog system for air travel information service (ATIS) is investigated. The experiments were carried out using a test database from 25 speakers (15 male and 10 female). There are 480 dialogs, containing 3038 sentences in the test database. The experimental results show that the speech act identification rate can be improved by 10.5% using the FFCMM and speech act verification with a rejection rate of 6% compared to a baseline system.