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

fYear

2015

fDate

3-5 March 2015

Firstpage

217

Lastpage

221

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on

Conference_Location

Mashhad

Print_ISBN

978-1-4799-8817-4

Type

conf

DOI

10.1109/AISP.2015.7123530

Filename

7123530