17,126 research outputs found
Continuous Interaction with a Virtual Human
Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking – and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACE’10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access
Amelioration vs. Perversion
Words change meaning, usually in unpredictable ways. But some words’ meanings are revised intentionally. Revisionary projects are normally put forward in the service of some purpose – some serve specific goals of inquiry, and others serve ethical, political or social aims. Revisionist projects can ameliorate meanings, but they can also pervert. In this paper, I want to draw attention to the dangers of meaning perversions, and argue that the self-declared goodness of a revisionist project doesn’t suffice to avoid meaning perversions. The road to Hell, or to horrors on Earth, is paved with good intentions. Finally and more importantly, I want to demarcate what meaning perversions are. This, I hope, can help us assess the moral and political legitimacy of revisionary projects
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Speech is the fundamental mode of human communication, and its synthesis has
long been a core priority in human-computer interaction research. In recent
years, machines have managed to master the art of generating speech that is
understandable by humans. But the linguistic content of an utterance
encompasses only a part of its meaning. Affect, or expressivity, has the
capacity to turn speech into a medium capable of conveying intimate thoughts,
feelings, and emotions -- aspects that are essential for engaging and
naturalistic interpersonal communication. While the goal of imparting
expressivity to synthesised utterances has so far remained elusive, following
recent advances in text-to-speech synthesis, a paradigm shift is well under way
in the fields of affective speech synthesis and conversion as well. Deep
learning, as the technology which underlies most of the recent advances in
artificial intelligence, is spearheading these efforts. In the present
overview, we outline ongoing trends and summarise state-of-the-art approaches
in an attempt to provide a comprehensive overview of this exciting field.Comment: Submitted to the Proceedings of IEE
Unveiling Affective Signals
The ability to process and, subsequently, understand affective signals is the core of emotional intelligence and empathy. However, more than a decade of research in affective computing has shown that it is hard to develop computational models of this process. We pose that the solution for this problem lays in a better understanding of how to process these affective signals. This article introduces a symposium that brought together various approaches towards unveiling affective signals. As such, it is envisioned to be a springboard for affective computing
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
- …