Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Conversational Sensing

Recent developments in sensing technologies, mobile devices and context-aware user interfaces have made it possible to represent information fusion and situational awareness as a conversational process among actors - human and machine agents - at or near the tactical edges of a network. Motivated by use cases in the domain of security, policing and emergency response, this paper presents an approach to information collection, fusion and sense-making based on the use of natural language (NL) and controlled natural language (CNL) to support richer forms of human-machine interaction. The approach uses a conversational protocol to facilitate a flow of collaborative messages from NL to CNL and back again in support of interactions such as: turning eyewitness reports from human observers into actionable information (from both trained and untrained sources); fusing information from humans and physical sensors (with associated quality metadata); and assisting human analysts to make the best use of available sensing assets in an area of interest (governed by management and security policies). CNL is used as a common formal knowledge representation for both machine and human agents to support reasoning, semantic information fusion and generation of rationale for inferences, in ways that remain transparent to human users. Examples are provided of various alternative styles for user feedback, including NL, CNL and graphical feedback. A pilot experiment with human subjects shows that a prototype conversational agent is able to gather usable CNL information from untrained human subjects

An architecture for fluid real-time conversational agents: Integrating incremental output generation and input processing

Kopp S, van Welbergen H, Yaghoubzadeh R, Buschmeier H. An architecture for fluid real-time conversational agents: Integrating incremental output generation and input processing. Journal on Multimodal User Interfaces. 2014;8:97-108.Embodied conversational agents still do not achieve the fluidity and smoothness of natural conversational interaction. One main reason is that current system often respond with big latencies and in inflexible ways. We argue that to overcome these problems, real-time conversational agents need to be based on an underlying architecture that provides two essential features for fast and fluent behavior adaptation: a close bi-directional coordination between input processing and output generation, and incrementality of processing at both stages. We propose an architectural framework for conversational agents [Artificial Social Agent Platform (ASAP)] providing these two ingredients for fluid real-time conversation. The overall architectural concept is described, along with specific means of specifying incremental behavior in BML and technical implementations of different modules. We show how phenomena of fluid real- time conversation, like adapting to user feedback or smooth turn-keeping, can be realized with ASAP and we describe in detail an example real-time interaction with the implemented system

Quantitative analysis of backchannels uttered by an interviewer during neuropsychological tests

International audienceThis paper examines in detail the backchannels uttered by a French professional interviewer during a neuropsychological test of verbal memories. These backchannels are short utterances such as oui, d'accord, uhm, etc. They are mainly produced here to encourage subjects to retrieve a set of words after their controlled encoding. We show that the choice of lexical items, their production rates and their associated prosodic contours are influenced by the subject performance and conditioned by the protocol

flexdiam – flexible dialogue management for problem-aware, incremental spoken interaction for all user groups (demo paper)

Yaghoubzadeh R, Kopp S. flexdiam – flexible dialogue management for problem-aware, incremental spoken interaction for all user groups (demo paper). In: Proceedings of the 7th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT 2016). 2016: 87-90

Conversational Assistants for Elderly Users – The Importance of Socially Cooperative Dialogue

Kopp S, Brandt M, Buschmeier H, et al. Conversational Assistants for Elderly Users – The Importance of Socially Cooperative Dialogue. In: André E, Bickmore T, Vrochidis S, Wanner L, eds. Proceedings of the AAMAS Workshop on Intelligent Conversation Agents in Home and Geriatric Care Applications co-located with the Federated AI Meeting. CEUR Workshop Proceedings. Vol 2338. Aachen: RWTH; 2018: 10–17.Conversational agents can provide valuable cognitive and/or emotional assistance to elderly users or people with cognitive impairments who often have difficulties in organizing and following a structured day schedule. Previous research showed that a virtual assistant that can interact in spoken language would be a desirable help for those users. However, these user groups pose specific requirements for spoken dialogue interaction that existing systems hardly meet. This paper presents work on a virtual conversational assistant that was designed for, and together with, elderly as well as cognitively handicapped users. It has been specifically developed to enable ‘socially cooperative dialogue’ – adaptive and aware conversational interaction in which mutual understanding is co-constructed and ensured collaboratively. The technical approach is described and results of evaluation studies are reported