Multilingual spoken-language understanding in the MIT Voyager system

Multimodal systems have attained increased attention in recent years, which has made possible important improvements in the technologies for recognition, processing, and generation of multimodal information. However, there are still many issues related to multimodality which are not clear, for example, the principles that make it possible to resemble human-human multimodal communication. This chapter focuses on some of the most important challenges that researchers have recently envisioned for future multimodal interfaces. It also describes current efforts to develop intelligent, adaptive, proactive, portable and affective multimodal interfaces.

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“…This architecture has been used to set up, among others, the MIT's Voyager and Jupiter systems (Glass et al, 1995;Zue et al, 2000).…”

Section: Architectures For the Design Of Multimodal Interfacesmentioning

confidence: 99%

On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces

Griol

Callejas

López-Cózar

et al. 2012

Speech, Image, and Language Processing for Human Computer Interaction

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“…One of the most wide-spread is information retrieval. Some sample applications are tourist and travel information [8,14], weather forecast over the phone [23], speech controlled telephone banking systems [12,13], conference help [5,10], etc. They have also been used for education and training, particularly in improving phonetic and linguistic skills: assistance and guidance to F18 aircraft personnel during maintenance tasks [4], dialog applications for computer-aided speech therapy with different language pathologies [2].…”

Section: Case Study Ii: Railway Station Scenariomentioning

confidence: 99%

Multi-agent System (MAS) Applications in Ambient Intelligence (AmI) Environments

Sánchez-Pi

Mangina

Carbó

et al. 2010

Advances in Intelligent and Soft Computing

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Abstract. Research in context-aware systems has been moving towards reusable and adaptable architectures for managing more advanced human-computer interfaces. Ambient. Intelligence (AmI) investigates computer-based services, which are ubiquitous and based on a variety of objects and devices. Their intelligent and intuitive interfaces act as mediators through which people can interact with the ambient environment. In this paper we present an agent-based architecture which supports the execution of agents in AmI environments. Two case studies are also presented, an airport information system and a railway information system, which uses spoken conversational agents to respond to the user's requests using the contextual information that includes the location information of the user.

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“…One of the most wide-spread applications is information retrieval. Some sample applications are tourist-and-travel information (Glass et al, 1995), weather forecast over the phone (Zue et al, 2000), speech-controlled telephone banking systems (Melin, Sandell, and Ihse 2001), conference help , and so forth. They have also been used for education and training, particularly in improving phonetic and linguistic skills; assistance and guidance to F18 aircraft personnel during maintenance tasks (Bohus and Rudnicky, 2005); and dialog applications for computer-aided speech therapy with different language pathologies (Vaquero et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

An Automatic Dialog Simulation Technique to Develop and Evaluate Interactive Conversational Agents

Griol

Carbó

Molina

2013

Applied Artificial Intelligence

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During recent years, conversational agents have become a solution to provide straightforward and more natural ways of retrieving information in the digital domain. In this article, we present an agent based dialog simulation technique for learning new dialog strategies and evaluating con versational agents. Using this technique, the effort necessary to acquire data required to train the dialog model and then explore new dialog strategies is considerably reduced. A set of measures has also been defined to evaluate the dialog strategy that is automatically learned and to compare different dialog corpora. We have applied this technique to explore the space of possible dialog stra tegies and evaluate the dialogs acquired for a conversational agent that collects monitored data from patients suffering from diabetes. The results of the comparison of these measures for an initial corpus and a corpus acquired using the dialog simulation technique show that the conversational agent reduces the time needed to complete the dialogs and improve their quality, thereby allowing the conversational agent to tackle new situations and generate new coherent answers for the situations already present in an initial model.

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Multilingual spoken-language understanding in the MIT Voyager system

Cited by 85 publications

References 1 publication

On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces

On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces

Multi-agent System (MAS) Applications in Ambient Intelligence (AmI) Environments

An Automatic Dialog Simulation Technique to Develop and Evaluate Interactive Conversational Agents

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