David Griol

A statistical approach to spoken dialog systems design and evaluation

In this paper, we present a statistical approach for the development of a dialog manager and for learning optimal dialog strategies. This methodology is based on a classification procedure that considers all of the previous history of the dialog to select the next system answer. To evaluate the performance of the dialog system, the statistical approach for dialog management has been extended to model the user behavior. The statistical user simulator has been used for the evaluation and improvement of the dialog strategy. Both the user model and the system model are automatically learned from a training corpus that is labeled in terms of dialog acts. New measures have been defined to evaluate the performance of the dialog system. Using these measures, we evaluate both the quality of the simulated dialogs and the improvement of the new dialog strategy that is obtained with the interaction of the two modules. This methodology has been applied to develop a dialog manager within the framework of the DIHANA project, whose goal is the design and development of a dialog system to access a railway information system using spontaneous speech in Spanish. We propose the use of corpus-based methodologies to develop the main modules in the dialog system.

A stochastic approach to dialog management

In this article, we present an approach to the development of a stochastic dialog manager. The model used by this dialog manager to generate its turns takes into account both the last turns of the user and system, and the information supplied by the user throughout the dialog. As the space of situations that can be presented in the dialogs is too large, some techniques for reducing this space have been proposed. This system has been developed in the DIHANA project, whose goal is the design and development of a dialog system to access a railway information system using spontaneous speech in Spanish. A training corpus of 900 dialogs, that was acquired through the Wizard of Oz, was used to learn the models. An evaluation of the dialog manager is also presented

Recognition and understanding simulation for a spoken dialog corpus acquisition

Since the design and acquisition of a new dialog corpus is a complex task, new methods to facilitate this task are necessary. In this paper, we present a methodology to make use of our previous work within the framework of dialog systems in order to acquire a dialog corpus for a new domain. The main idea is the simulation of recognition and understanding errors in the acquisition of the new dialog corpus. This simulation is based on the analysis of such errors in a previously acquired corpus and the definition of a correspondence table among the concepts and attributes of both tasks. This correspondence table is based on the similarity of semantic meaning and frequencies. Finally, the application of this methodology is illustrated in some examples.

A Dialog Management Methodology Based on Neural Networks and Its Application to Different Domains

In this paper, we present a statistical approach for dialog management within the framework of two different domains. The dialog model, that is automatically learned from a data corpus, is based on the use of a classification process to generate the next system answer. A neural network classifier is used for the selection process. This methodology has been applied in a spoken dialog system that provides railway information. The definition of an extended methodology that takes into account new system functionalities and its application for developing a dialog system for booking sports facilities is also described.

A statistical user simulation technique for the improvement of a spoken dialog system

In this paper, we present a statistical approach for the automatic generation of dialogs by means of a user simulator. This technique can be used to generate dialogs with reduced effort, facilitating the evaluation and improvement of spoken dialog systems. In our approach for user simulation, the user answer is selected taking into account the history of the dialog and the last system turn, as well as the objective(s) set for the dialog. The user model is automatically learned from a training corpus that is labeled in terms of dialog acts. This methodology has been evaluated within the framework of the DIHANA project, whose goal is the design and development of a dialog system to access a railway information system using spontaneous speech in Spanish.

A Two Phases Statistical Approach for Dialog Management

In this paper, we present a statistical methodology for dialog management. This methodology is based on a classification procedure that considers all of the previous history of the dialog and the result of the queries to the module that controls the application. The classification procedure is divided into two phases to generate the next system turn. We summarize the work that has been carried out to apply this methodology for developing a dialog system for booking sports facilities. A dialog simulation technique has been used to acquire a dialog corpus for this task and to develop a dialog manager following the proposed methodology. The final model has been evaluated using a corpus recorded for the task with real users.

A Platform for the Development of Spoken Dialog Systems

In this paper, we present a platform for the development of dialog systems. The architecture is organized in modules, each of which accomplishes a specific objective. Communication among modules is done by means of transmission of XML data packages through sockets. This platform allows for multimodal communication and permits an easy interchange of modules, so that the change of language or task is straightforward. In our system, most of the modules use stochastic models for the representation of the different knowledge sources involved in the dialog process. These models are automatically learnt from corpora of training data. We also present a dialog system developed with this platform. The task of the dialog system presented is the access to information about train timetables and prices of trains in Spanish.