In this paper, the simulated annealing Q-learning (SA-Q) algorithm is adopted to automatically learn the optimal dialogue strategy of a spoken dialogue system. Several simulations and experiments considering different user behaviors and speech recognizer performance are conducted to verify the effectiveness of the SA-Q learning approach. Moreover, the automatically learned strategy is applied t...

Educational tools are essential in teaching the field of Spoken Dialogue Systems given the complexity and variety of disciplines involved. This paper describes DUDE, a Dialogue and Understanding Development Environment that enables researchers and students to efficiently create Information State Update (ISU) Spoken Dialogue Systems using large scale databases with minimal programming and gramma...

Spoken language communication between human and machines has become a challenge in research and technology. In particular, enabling the health care robots with spoken language interface is of great attention. Recently due to uncertainty characterizing dialogues, there has been interest for modelling the dialogue manager of spoken dialogue systems using Partially Observable Markov Decision Proce...

Spoken dialogue management strategy optimization by means of Reinforcement Learning (RL) is now part of the state of the art. Yet, there is still a clear mismatch between the complexity implied by the required naturalness of dialogue systems and the inability of standard RL algorithms to scale up. Another issue is the sparsity of the data available for training in the dialogue domain which can ...

This paper describes a spoken dialogue processing, which includes the learning using spoken dialogue examples. Most of the spoken dialogue systems up to the present are task-oriented, where the processing is based on the prespecified generation rules and database. It is then difficult to handle various topics, such as miscellaneous talks in daily dialogue. In the proposed method, the dialogue b...

The application of machine learning methods to the dialogue management component of spoken dialogue systems is a growing research area. Whereas traditional methods use handcrafted rules to specify a dialogue policy, machine learning techniques seek to learn dialogue behaviours from a corpus of training data. In this paper, we identify the properties of a corpus suitable for training machine-lea...

Spoken medical dialogue is a valuable source of information, and it forms a foundation for diagnosis, prevention and therapeutic management. However, understanding even a perfect transcript of spoken dialogue is challenging for humans because of the lack of structure and the verbosity of dialogues. This work presents a first step towards automatic analysis of spoken medical dialogue. The backbo...

This paper describes a statistically motivated framework for performing real-time dialogue state updates and policy learning in a spoken dialogue system. The framework is based on the partially observable Markov decision process (POMDP), which provides a well-founded, statistical model of spoken dialogue management. However, exact belief state updates in a POMDP model are computationally intrac...

Given the growing complexity of tasks that spoken dialogue systems are trying to handle, Reinforcement Learning (RL) has been increasingly used as a way of automatically learning the best policy for a system to make. While most work has focused on generating better policies for a dialogue manager, very little work has been done in using RL to construct a better dialogue state. This paper presen...

In designing a spoken dialogue system, developers need to specify the actions a system should take in response to user speech input and the state of the environment based on observed or inferred events, states, and beliefs. This is the fundamental task of dialogue management. Researchers have recently pursued methods for automating the design of spoken dialogue management using machine learning...