Some background on dialogue management and conversational speech for dialogue systems

Wilks, Yorick; Catizone, Roberta; Worgan, Simon F.; Turunen, Markku

doi:10.1016/j.csl.2010.03.001

Cited by 34 publications

(16 citation statements)

References 34 publications

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“…Although dialog management is only a part of the development cycle of spoken dialog systems, it can be considered one of the most demanding tasks given that it decides the next action of the system and encapsulates the logic of the speech application [4]. The selection of a specific system action depends on multiple factors, such as the output of the speech recognizer (e.g., measures that define the reliability of the recognized information), the dialog interaction and previous dialog history (e.g., the number of repairs carried out so far), the application domain (e.g., guidelines for customer service), knowledge about the users, and the responses and status of external back-ends, devices, and data repositories.…”

Section: Introductionmentioning

confidence: 99%

Influence of the data codification when applying evolving classifiers to develop spoken dialog systems

Iglesias

Griol

Ledezma

et al. 2014

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

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In this paper we present a study of the influence of the representation of the data when applying evolving classifiers in a specific classification task. In particular, we consider an evolving classifier for the development of a spoken dialog system interacting in a practical domain. In order to conduct this study, we will first introduce an approach based on evolving fuzzy systems (EFS) which is employed to select the next system action of the dialog system. This classifier takes into account a set of evolving fuzzy rules which are automatically obtained using evolving systems. The reason for using EFS in this domain is that we can process streaming data on-line in real time and the structure and operation of the dialog model can dynamically change by considering the interaction of the dialog system with its users. Since we want to apply this evolving approach in a real domain, our proposal considers the data supplied by the user throughout the complete dialog history and the confidence measures provided by the recognition and understanding modules of the system. The paper is focused on the study of the influence of the codification of this input data to achieve the best performance of the proposed approach. To do this, we have completed this study for a real spoken dialog system providing railway information.

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Section: Introductionmentioning

confidence: 99%

Influence of the data codification when applying evolving classifiers to develop spoken dialog systems

Iglesias

Griol

Ledezma

et al. 2014

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

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“…Although dialog manage ment is only a part of the development cycle of spoken dialog systems, it can be considered one of the most demanding tasks given that this module encapsulates the logic of the speech application [62]. This way, the design of an appropriate dialog management strategy is at the core of dialog system engineering.…”

Section: Related Workmentioning

confidence: 99%

A proposal for the development of adaptive spoken interfaces to access the Web

2015

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Spoken dialog systems have been proposed as a solution to facilitate a more natural human machine interaction. In this paper, we propose a framework to model the user's intention during the dialog and adapt the dialog model dynamically to the user needs and preferences, thus developing more efficient, adapted, and usable spoken dialog systems. Our framework employs statistical models based on neural networks that take into account the history of the dialog up to the current dialog state in order to predict the user's intention and the next system response. We describe our proposal and detail its application in the Let's Go spoken dialog system.

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“…This new paradigm fostered a change from proprietary ad hoc architectures to others that fulfilled the purpose of reusability by means of modular "plug-and-play" models, such as different agent-based and modular architectures (Wilks, Catizone, Worgan, & Turunen, 2011), e.g., the Galaxy Communicator (O'Neill, Hanna, Liu, Greer, & McTear, 2005;Seneff, 2002) and Olympus, which is based on Galaxy (Bohus et al, 2007).…”

Section: Development Paradigmsmentioning

confidence: 99%

Review of spoken dialogue systems

López-Cózar

Callejas

Griol

et al. 2014

loquens

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Spoken dialogue systems are computer programs developed to interact with users employing speech in order to provide them with specific automated services. The interaction is carried out by means of dialogue turns, which in many studies available in the literature, researchers aim to make as similar as possible to those between humans in terms of naturalness, intelligence and affective content.In this paper we describe the fundaments of these systems including the main technologies employed for their development. We also present an evolution of this technology and discuss some current applications. Moreover, we discuss development paradigms, including scripting languages and the development of conversational interfaces for mobile apps.The correct modelling of the user is a key aspect of this technology. This is why we also describe affective, personality and contextual models. Finally, we address some current research trends in terms of verbal communication, multimodal interaction and dialogue management.Keywords: dialogue; language understanding; dialogue management; natural language generation; speech synthesis. RESUMEN:Sistemas de diálogo: una revisión.-Los sistemas de diálogo son programas de ordenador desarrollados para interaccionar con los usuarios mediante habla, con la finalidad de proporcionarles servicios automatizados. La interacción se lleva a cabo mediante turnos de un tipo de diálogo que, en muchos estudios existentes en la literatura, los investigadores intentan que se parezca lo más posible al diálogo real que se lleva a cabo entre las personas en lo que se refiere a naturalidad, inteligencia y contenido afectivo.En este artículo describimos los fundamentos de esta tecnología, incluyendo las tecnologías básicas que se utilizan para implementar este tipo de sistemas. También presentamos una evolución de la tecnología y comentamos algunas aplicaciones actuales. Asimismo, describimos paradigmas de interacción, incluyendo lenguajes de script y desarrollo de interfaces conversacionales para aplicaciones móviles.Un aspecto clave de esta tecnología consiste en realizar un correcto modelado del usuario. Por este motivo, discutimos diversos modelos afectivos, de personalidad y contextuales. Finalmente, comentamos algunas líneas de investigación actuales relacionadas con la comunicación verbal, interacción multimodal y gestión del diálogo.Palabras clave: diálogo; comprensión del lenguaje; gestión del diálogo; generación del lenguaje natural; síntesis del habla.

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Some background on dialogue management and conversational speech for dialogue systems

Cited by 34 publications

References 34 publications

Influence of the data codification when applying evolving classifiers to develop spoken dialog systems

Influence of the data codification when applying evolving classifiers to develop spoken dialog systems

A proposal for the development of adaptive spoken interfaces to access the Web

Review of spoken dialogue systems

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