Speech act modeling in a spoken dialogue system using fuzzy hidden Markov model and bayes' decision criterion
نویسندگان
چکیده
In this paper, a corpus-based fuzzy hidden Markov model (FHMM) is proposed to model the speech act in a spoken dialogue system. In the training procedure, 29 FHMM’s are defined and trained, each representing one speech act in our approach. In the identification process, the Viterbi algorithm is used to find the top M candidate speech acts. Then Bayes’ decision criterion, which stores the relationship between the phrase and the speech act, is employed to choose the most probable speech act from the top M speech acts. In order to evaluate the proposed method, a spoken dialogue system for air travel information service is investigated. The experiments were carried out using a test database from 25 speakers (15 male and 10 female). There are 120 dialogues, which contains 725 sentences in the test database. The experimental results show that the correct response rate can achieve about 82.7% using the FHMM and the Bayes’ decision criterion.
منابع مشابه
Robust dialogue act detection based on partial sentence tree, derivation rule, and spectral clustering algorithm
A novel approach for robust dialogue act detection in a spoken dialogue system is proposed. Shallow representation named partial sentence trees are employed to represent automatic speech recognition outputs. Parsing results of partial sentences can be decomposed into derivation rules, which turn out to be salient features for dialogue act detection. Data-driven dialogue acts are learned via an ...
متن کاملSub-lexical Dialogue Act Classification in a Spoken Dialogue System Support for the Elderly with Cognitive Disabilities
This paper presents a dialogue act classification for a spoken dialogue system that delivers necessary information to elderly subjects with mild dementia. Lexical features have been shown to be effective for classification, but the automatic transcription of spontaneous speech demands expensive language modeling. Therefore, this paper proposes a classifier that does not require language modelin...
متن کاملLearning User Intentions in Spoken Dialogue Systems
A common problem in spoken dialogue systems is finding the intention of the user. This problem deals with obtaining one or several topics for each transcribed, possibly noisy, sentence of the user. In this work, we apply the recent unsupervised learning method, Hidden Topic Markov Models (HTMM), for finding the intention of the user in dialogues. This technique combines two methods of Latent Di...
متن کاملInferring linguistic structure in spoken language
We demonstrate the applications of Markov Chains and HMMs to modeling of the underlying structure in spontaneous spoken language. Experiments with supervised training cover the detection of the current dialog state and identi cation of the speech act as used by the speech translation component in our JANUS Speech-to-Speech Translation System. HMM training with hidden states is used to uncover o...
متن کاملFlexible speech act identification of spontaneous speech with disfluency
This paper describes an approach for flexible speech act identification of spontaneous speech with disfluency. In this approach, semantic information, syntactic structure, and fragment features of an input utterance are statistically encapsulated into a proposed sp eech act hidden Markov model (SAHMM) to characterize the speech act. To deal with the disfluency problem in a sparse training corpu...
متن کامل