Paul Heisterkamp

Affective Dialogue Systems

The monitoring of emotional user states can help to assess the progress of human-machine-communication. If we look at specific databases, however, we are faced with several problems: users behave differently, even within one and the same setting, and some phenomena are sparse; thus it is not possible to model and classify them reliably. We exemplify these difficulties on the basis of SympaFly, a database with dialogues between users and a fully automatic speech dialogue telephone system for flight reservation and booking, and discuss possible remedies.

Dialogue Management for Telephone Information Systems

A distributed approach to spoken dialogue management for real-time telephone information systems is outlined.

Units of dialogue management: an example

The paper concerns dialogue management for spoken dialogue. We show why we do not use speech act related units or intentions. We base our approach on belief states of the system. Layered units are used to construct a pragmatic interpretation of these states and to determine the dialogue continuation as a local optimisation over a set of dynamic dialogue goals. We point to systems that successfully employ this approach.

The SENECA spoken language dialogue system

Abstract This article describes a speech-based user interface to a wide range of entertainment, navigation and communication applications in mobile environments by means of human-machine dialogues. The system has been developed in the framework of the EU-project SENECA. It uses noise reduction, speech recognition, and dialogue processing techniques. One interesting aspect relies in the fact that low speech recognition confidence and word-level ambiguities are compensated by engaging flexible clarification dialogues with the user. The SENECA system demonstrator has been evaluated by means of user tests. With speech input, road safety, especially for complex tasks is significantly improved. Compared to manual input, the feeling of being distracted from driving is less important with speech.

Safety and operating issues for mobile human-machine interfaces

In this paper we present recent research and development efforts carried out at DaimlerChrysler to integrate speech technology for use in mobile environments, notably in cars. Speech undeniably has the potential to considerably improve the safety and user friendliness of Human-machine interfaces, especially when complex technical functionalities and devices need to be accessed. As an example, we describe Linguatronic, a commercially available in-vehicle Command&Control dialog system. In addition, the SmartKom project demonstrates advanced concepts for intuitive multimodal computer interfaces in three different application scenarios.

Intelligent dialog overcomes speech technology limitations: the SENECa example

We present a primarily speech-based user interface to a wide range of entertainment, navigation and communication applications for use in vehicles. The multimodal dialog en ables the system to uniquely identify one of 79,000 place name variants using an active vocabulary of only 3,000 words at any given time. Low confidence in speech recog nition and word-level ambiguities are compensated for in flexible clarification dialogs with the user. The underlying dialog concept was developed in the framework of the EU-project SENECa. Some recent evalua tion results of the SENECa system demonstrator are discussed in the paper

Some Notes on the Complexity of Dialogues

The purpose of this paper is twofold. First, we describe some complexity aspects of spoken dialogue. It is shown that, given the internal setting of our dialogue system, it is impossible to test even a small percentage of the theoretically possible utterances in a reasonable amount of time. An even smaller part of possible dialogues can thus be tested. Second, an approach for early testing of the dialogue manager of a dialogue system, without the complete system being put together, is described.

Design, Implementation and Evaluation of the SENECA Spoken Language Dialogue System

This chapter describes a speech-based user interface to a wide range of entertainment, navigation and communication applications in mobile environments by means of human-machine dialogues. The system has been developed in the framework of the EU project SENECA. It uses noise reduction, speech recognition and dialogue processing techniques. An interesting aspect relies in the fact that speech recognition results with low confidence and word-level ambiguities are compensated by engaging flexible clarification dialogues with the user. The SENECA system has been evaluated by means of user tests. With speech input, road safety, especially for complex tasks is significantly improved. The feeling of being distracted from driving is smaller with speech input than with manual input.

Speak to the Target Audience – Some Observations and Notes on the Pragmatics of Spoken Dialog Systems

In this book, most chapters deal in one way or another, with techniques to build and improve spoken dialogue systems that deliver the best possible service to their users. The sub-title of this collection implicitly assumes that the people who call in to telephone-based services or who use multi-modal interaction with mobile systems are this target audience. And, of course, under scientific, technical and engineering aspects, this is undoubtedly true.

Natürliche Sprache - ein multimedialer Träger von Information InfoPort - ein Projekt zur Überbrückung von Medienbrüchen bei der Verarbeitung sprachlicher Information

Es wird ein Projekt vorgestellt, das zum Ziel eine medienunabhangige Verarbeitung sprachlicher Information hat. Sprachliche Information erscheint in geschriebener oder gesprochener Form (Medien: Papier, Fax, elektonischer Text, e-mail, voice-mail, Telefon,…). Die Einsatzgebiete sind Retrieval, aktive Informationsvermittlung und Assistenz. Bereits realisierte Anwendungen liegen in den Bereichen Analyse von schriftlichen Anfragen (Geschaftsberichte), telefonische Auskunftssysteme und Datenbankzugriff (STORM). Die eingesetzten Techniken sind einerseits signalnahe Mustererkennungsalgorithmen zum Hypothetisieren von Wortern aus Bildern oder dem Sprachsignal (Dokumentbild- analyse, OCR, HMM), anderseits wissensbasierte Techniken zur Interpretation der sprachlichen Information. Eine robuste Verarbeitung verlangt eine enge Verzahnimg von Erkennung und Interpretation. Auch eine bruchteilhafte Erkennung mus interpretiert werden.