This paper describes a technique to include acoustic confidence scores as returned by automated speech recognisers in generic semantic representations. The method we propose requires only minimal changes to an existing grammar used for speech applications. Special attention is paid to the treatment of multi-word lexemes and combining several (N-best) speech recognition results into one semantic representation. The approach has been implemented and tested using the Nuance speech recognition software and a chart parser, in the formalism of underspecified discourse representations. The potential relevance of confidence scores in rich semantic representations is illustrated by generating more flexible clarification questions in dialogue systems.

Abstract. This paper describes ongoing research in order to enhance our Domain Knowledge Manager (DKM) that is a module of a multi-propose Spoken Dialogue System (SDS) architecture. The application domain is materialized as an arbitrary set of devices, such as household appliances, providing useful tasks to the SDS users. Our main contribution is a DKM advisor service, which suggests the best task-device pairs to satisfy a request. Additionally, we also propose a DKM recognizer service to identify the domain’s concepts from a natural language request. These services use as knowledge source a domain model, to obtain knowledge about devices and the tasks they provide. The implementation of these services allows the DKM to provide a high-level and easy to use small interface, instead of a conventional service interface with several remote procedures/methods. These services have been tested into a domain simulator. Our contributions try to reach SDS domain portability issues. 1

Abstract – A pervasive computing environment consists typically of a large heterogeneous collection of networked devices. This paper describes the use of lexical knowledge to improve a pervasive computing environment. In an ongoing research project, we are exploring ways to enable non-technical users to manage and control their home environment that is particularly hostile. We assume that each device belonging to the pervasive environment has its own knowledge model, linked to lexical resources, with the purpose of defining a semantic interface. This approach tries to reach the pervasive essence of the natural language. The coverage of handmade lexical resources is limited, coverage problems remain for applications involving specific domains or involving multiple languages. Our recent efforts are directed towards the technology development, focusing on devices that are household appliances. This work is a contribution to facilitate, specially: the generation of multilingual device descriptions, the automatic build of device’s graphical user interfaces, and on the fly adaptation of a spoken dialogue system to the pervasive environment.

As household appliances grow in complexity and sophistication, they become harder and harder to use, particularly because of their tiny display screens and limited keyboards. This paper describes a strategy for building natural language interfaces to appliances that circumvents these problems. Our approach leverages decades of research on planning and natural language interfaces to databases by reducing the appliance problem to the database problem; the reduction provably maintains desirable properties of the database interface. The paper goes on to describe the implementation and evaluation of the EXACT interface to appliances, which is based on this reduction. EXACT maps each English user request to an SQL query, which is transformed to create a PDDL goal, and uses the Blackbox planner [13] to map the planning problem to a sequence of appliance commands that satisfy the original request. Both theoretical arguments and experimental evaluation show that EXACT is highly reliable.

this document may be reproduced or transmitted in any form, or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission from the copyright owner

We explore natural and calm interfaces for configuring ubiquitous computing environments. A natural interface should enable the user to name a desired configuration and have the system enact that configuration. Users should be able to use familiar names for configurations without learning, which implies the mapping from names to configurations is many-to-one. Instead of users learning the environment’s command language, the system simultaneously learns common configurations and infers the keywords that are most salient to them. We call this the SNAC problem (Simultaneous Naming and Configuration). As a case study, we contrast speech and GUI interfaces for workspace lighting control on a large array of individually-controllable lights. In our design process we have used a lo-fidelity text-based study, a speech-based training data collection study, an evaluation of the deployed live system and are conducting a longer evaluation of the deployed system.