Attentive listening systems are designed to let people, especially senior people, keep talking to maintain communication ability and mental health. This paper addresses key components of an attentive listening system which encourages users to talk smoothly. First, we introduce continuous prediction of end-of-utterances and generation of backchannels, rather than generating backchannels after end-point detection of utterances. This improves subjective evaluations of backchannels. Second, we propose an effective statement response mechanism which detects focus words and responds in the form of a question or partial repeat. This can be applied to any statement. Moreover, a flexible turn-taking mechanism is designed which uses backchannels or fillers when the turnswitch is ambiguous. These techniques are integrated into a humanoid robot to conduct attentive listening. We test the feasibility of the system in a pilot experiment and show that it can produce coherent dialogues during conversation.

We demonstrate dialogues with an autonomous android ERICA, who has an appearance like a human being. Currently, ERICA plays two social roles: a laboratory guide and a counselor. It is designed to follow the protocols of human dialogue to make the user comfortable: (1) having a chat before the main talk, (2) proactively asking questions, and (3) conveying proper feedbacks. The combination of the human-like appearance and the appropriate behaviors according to her social roles allows for symbiotic human-robot interaction.

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This paper reports on the results of the first lab trials evaluating the vAssist (Voice Controlled Assistive Care and Communication Services for the Home) system prototype with Italian users.vAssist is an European Project aiming to provide specific voice controlled home care and communication services for elderly. An important vAssist objective is a multilingual Voice User Interface (VUI) in three different languages: Italian, French and German. Lab trials were foreseen in these three different countries to assess the vAssist VUI prototype on realistic user expectations and requirements. The assessment was made letting 43 Italian elderly interact with the VUI prototype in 4-5 defined scenarios, exploiting a Wizard-of-Oz (WoZ) paradigm and administering to them three questionnaires aimed to measure their perception of the system's usability, learnability and intuitivity. Qualitative and quantitative scores suggested that VUIs are very powerful communication interfaces and were greatly appreciated because of the simplification they provide in the elder everyday use of technological products, such as mobile phones, tablets, and computers.

Voice-based digital Assistants such as Apple's Siri and Google's Now are currently booming. Yet, despite their promise of being context-aware and adapted to a user's preferences and very distinct needs, truly personal assistants are still missing. In this paper we highlight some of the challenges in building personalized speech-operated assistive technology and propose a number of research and development directions we have undertaken in order to solve them. In particular we focus on natural language understanding and dialog management aspects as we believe that these parts of the technology pipeline require the biggest amount of augmentation.

Wizard of Oz (WOZ) prototyping employs a human wizard to simulate anticipated functions of a future system. In Natural Language Processing this method is usually used to obtain early feedback on dialogue designs, to collect language corpora, or to explore interaction strategies. Yet, existing tools often require complex client-server configurations and setup routines, or suffer from compatibility problems with different platforms. Integrated solutions, which may also be used by designers and researchers without technical background, are missing. In this paper we present a framework for multi-lingual dialog research, which combines speech recognition and synthesis with WOZ. All components are open source and adaptable to different application scenarios.

This research aims at providing Voice controlled Assistive (vAssist) Care and Communication Services for the Home to seniors suffering from fine-motor problems and/or chronic diseases. The constantly growing life expectancy of the European population increasingly asks for technological products that help seniors to manage their activities of daily living. In particular, we require solutions which offer interaction paradigms that fit the cognitive abilities of elderly users. Natural language-based access can be seen as one way of increasing the usability of these services. Yet, the construction of robust language technologies such as Automatic Speech Recognition and Natural Language Understanding does require sufficient domain specific interaction data. In this paper we describe how we plan to obtain the relevant corpus data for a set of different application scenarios, using the Wizard of Oz (WOZ) prototyping method. Using a publicly available WOZ tool we discuss how the integration of existing language technologies with a human wizard may help in designing a natural user interface for seniors and how such has the potential to underpin an iterative user-centred development process for languagebased applications.

vAssist (Voice Controlled Assistive Care and Communication Services for the Home) is a European project for which several research institutes and companies have been working on the development of adapted spoken interfaces to support home care and communication services. This paper describes the spoken dialog system that has been built. Its natural language understanding module includes a novel reference resolver and it introduces a new hierarchical paradigm to model dialog tasks. The user-centered approach applied to the whole development process led to the setup of several experiment sessions with real users. Multilingual experiments carried out in Austria, France and Spain are described along with the analyses and results in terms of both system performance and user experience. An additional experimental comparison of the RavenClaw and Disco-LFF dialog managers built into the vAssist spoken dialog system highlighted similar performance and user acceptance.