9,831 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
ANTIQUE: A Non-Factoid Question Answering Benchmark
Considering the widespread use of mobile and voice search, answer passage
retrieval for non-factoid questions plays a critical role in modern information
retrieval systems. Despite the importance of the task, the community still
feels the significant lack of large-scale non-factoid question answering
collections with real questions and comprehensive relevance judgments. In this
paper, we develop and release a collection of 2,626 open-domain non-factoid
questions from a diverse set of categories. The dataset, called ANTIQUE,
contains 34,011 manual relevance annotations. The questions were asked by real
users in a community question answering service, i.e., Yahoo! Answers.
Relevance judgments for all the answers to each question were collected through
crowdsourcing. To facilitate further research, we also include a brief analysis
of the data as well as baseline results on both classical and recently
developed neural IR models
Speech-based recognition of self-reported and observed emotion in a dimensional space
The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance
The Validation of Speech Corpora
1.2 Intended audience........................
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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