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On the Importance of Representations for Speech-Driven Gesture Generation: Extended Abstract
KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.ORCID-id: 0000-0001-9838-8848
Hokkai Gakuen University, Sapporo, Japan.
Aoyama Gakuin University, Sagamihara, Japan.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Tal, musik och hörsel, TMH.
Vise andre og tillknytning
2019 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper presents a novel framework for automatic speech-driven gesture generation applicable to human-agent interaction, including both virtual agents and robots. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our model takes speech features as input and produces gestures in the form of sequences of 3D joint coordinates representing motion as output. The results of objective and subjective evaluations confirm the benefits of the representation learning.

sted, utgiver, år, opplag, sider
The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2019. s. 2072-2074
Emneord [en]
Gesture generation; social robotics; representation learning; neural network; deep learning; virtual agents
HSV kategori
Forskningsprogram
Människa-datorinteraktion
Identifikatorer
URN: urn:nbn:se:kth:diva-251648OAI: oai:DiVA.org:kth-251648DiVA, id: diva2:1316254
Konferanse
International Conference on Autonomous Agents and Multiagent Systems (AAMAS '19), May 13-17, 2019, Montréal, Canada
Prosjekter
EACare
Forskningsfinansiär
Swedish Foundation for Strategic Research , RIT15-0107
Merknad

QC 20190515

Tilgjengelig fra: 2019-05-16 Laget: 2019-05-16 Sist oppdatert: 2019-05-22bibliografisk kontrollert

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Kucherenko, TarasHenter, Gustav EjeKjellström, Hedvig

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Totalt: 110 treff
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