Abstract
Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users.
Highlights
Dialogue systems are artificial systems able to hold a conversation with a human user, usually to achieve a certain objective through a natural language dialogue [1, 2]
In order to do so, 150 recruited users have followed a set of scenarios that specifies a set of objectives that must be fulfilled by the user at the end of the dialogue and are designed to include the complete set of functionalities previously described for the system
The number of recorded dialogues was 600: 300 dialogues were acquired by 75 users using the baseline version of the system and 300 dialogues were acquired by 75 users using the useradapted system
Summary
Dialogue systems are artificial systems able to hold a conversation with a human user, usually to achieve a certain objective (e.g., providing some information or fulfilling a task) through a natural language dialogue [1, 2]. To complete the adaptation a dialogue manager has been designed following a statistical approach based on neural networks that incorporates the user information generated by the user model along with the history of the dialogue. Both models can be computed from a dialogue corpus using a format defined to code the information in a data structure that considers only whether each piece of information has been provided or not by the user and with which confidence it has been recognized by the speech recognition and understanding modules.
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