Performance Evaluation of Automatic Gesture Generation System Using Bi-Directional LSTM on Humanoid Robot

DOI
  • HIYORI Kodai
    Graduate School of Information Science and Technology, Hokkaido University
  • ARAKI Kenji
    Graduate School of Information Science and Technology, Hokkaido University
  • HASEGAWA Dai
    Tokyo University of Technology (Currently: Hokkai Gakuen University)
  • YOSHIO Satoshi
    Graduate School of Information Science and Technology, Hokkaido University

Bibliographic Information

Other Title
  • 講義代行ロボットにおける双方向LSTMを用いたジェスチャ自動生成システムの性能評価

Description

<p>Conventional lecture substitution systems with humanoid robots use pre-defined gestures created by hand. Automatically generating these gestures makes it possible to create gestures without requiring expert knowledge and work, which is expected to lead to further progress in research on lecture substitution systems. This paper proposes an automatic gesture generation method which is expected to consider the semantic context of an utterance. Our proposed method is implemented by using a deep neural network with Bi-Directional LSTM units, applying filters for data correction, and axis conversion.</p>

Journal

Details 詳細情報について

  • CRID
    1390282763025679744
  • NII Article ID
    130007423533
  • DOI
    10.11517/pjsai.jsai2018.0_2c401
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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