Title :
A Persian spoken dialogue system using POMDPs
Author :
Mahmoudi, Hesam ; Homayounpour, Mohammad Mehdi
Author_Institution :
Lab. for Intell. Multimedia Process. (LIMP), Amirkabir Univ. of Technol., Tehran, Iran
Abstract :
This paper represents a statistically framework for a Persian spoken dialogue system. The framework is based on the Partially Observable Markov Decision Process (POMDP). A Bayesian network is used to represent the states of the POMDP model. It is shown that Bayesian approaches can improve the spoken dialogue system performance by handling uncertainties. Also Natural Actor Critic (NAC) algorithm is used for learning in spoken dialogue system and finally a framework for collecting training data is proposed. We compare the system with a handcrafted spoken dialogue system to show the efficiency of the proposed framework.
Keywords :
Bayes methods; Markov processes; learning (artificial intelligence); natural language processing; speech processing; statistical analysis; Bayesian network; NAC algorithm; POMDP; Persian spoken dialogue system; natural actor critic algorithm; partially observable Markov decision process; statistically framework; Bayes methods; Computational modeling; Error analysis; Noise; Speech; Training; Training data; Bayesian networks; Dialogue management; POMDP; Policy optimization; Spoken dialogue systems;
Conference_Titel :
Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on
Conference_Location :
Mashhad
Print_ISBN :
978-1-4799-8817-4
DOI :
10.1109/AISP.2015.7123530