Knowledge-Combining Methodology for Dialogue Design in Spoken Language Systems

Abstract

In this paper, we propose a strategy for designing dialogue managers in spoken dialogue systems for a restricted domain. This strategy combines several information sources intuition, observation and simulation, in order to maximize the adaptation within the system capability and the expectation of the user. These sources are combined by an iterative process consisting of five steps, where different dialogue alternatives are proposed and evaluated sequentially. The evaluation process includes different measures depending on the information required. Several measures are proposed and analyzed in each step. We also describe a user-modeling technique and an approach for designing the confirmation sub-dialogues based on recognition confidence measures. The knowledge-combining methodology is described and applied to a railway information system. In a subjective evaluation, users from the university gave the system a 3.9 score on a 5-point scale with an average call duration of 205 seconds. The employers of the railway company were more critical of the system. They gave it a score of 2.1 even though the system resolved more than half of the calls (57.8%) within an average call duration of three minutes (185 seconds).

Requirement Engineering Contributions to Voice User Interface

“…These systems can be defined by six features [11,12,18]: • Speech Recognition: process that allows a computer to convert an acoustic sign to a text, setting the user free from the WIMP -Windows, Icons, Menus, Pointers -interface restriction. This process includes the following steps: capturing the acoustic sign; converting this sign to digital components which are, then, divided into different sounds; classifying the sound, establishing all the possible combinations among the sound divisions and the phonetic representations; and searching for a word in the vocabulary that best fits this sound combination;…”

“…INTRODUCTION OICE User Interface Systems are able to, besides recognizing the user voice, understand what the user says and supply responses to these inputs, usually, in real time. The state-of-art in speech technology already allows the development of automatic systems designed to work in real conditions [18]. Enterprises such as Philips, AT&T, and IBM have been invested on the development of speech systems for restricted domain.…”