Titlebook: Situated Dialog in Speech-Based Human-Computer Interaction; Alexander Rudnicky,Antoine Raux,Teruhisa Misu Book 2016 Springer International

GEM

Takaaki Sugiyama,Kazunori Komatani,Satoshi Satoom novice to professional.Written by professionals in this f.Life As we Know It ["LAKI"] covers several aspects of Life, ranging from the prebiotic level, origin of life, evolution of prokaryotes to eukaryotes and finally to various affairs of human beings. Although it is hard to define Life, one ca

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neutralize

一瞥

om novice to professional.Written by professionals in this f.Life As we Know It ["LAKI"] covers several aspects of Life, ranging from the prebiotic level, origin of life, evolution of prokaryotes to eukaryotes and finally to various affairs of human beings. Although it is hard to define Life, one ca

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Simon Receveur,David Scheler,Tim Fingscheidtom novice to professional.Written by professionals in this f.Life As we Know It ["LAKI"] covers several aspects of Life, ranging from the prebiotic level, origin of life, evolution of prokaryotes to eukaryotes and finally to various affairs of human beings. Although it is hard to define Life, one ca

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Evaluation of In-Car SDS Notification Concepts for Incoming Proactive Eventse results show that there are significant differences when comparing the speech dialog concepts. Informing the user verbally achieves the best result concerning usability. Earcons are perceived to be the least distractive. The presence of an avatar was not accepted by the participants and led to an

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HAUNT

Entrainment in Pedestrian Direction Giving: How Many Kinds of Entrainment?in order to test whether some types of entrainment are preferred, either because they make the utterance more natural, or because humans perceive the system as more friendly. Our experimental results suggest that human judgements of naturalness are distinct from friendliness: entraining on a user’s

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A Turbo-Decoding Weighted Forward-Backward Algorithm for Multimodal Speech Recognitiontion task, our proposed method clearly outperforms a conventional coupled hidden-Markov model approach as well as an iterative state-of-the-art approach with up to 32.3 % relative reduction in word error rate.