• DocumentCode
    799234
  • Title

    Auditory learning: a developmental method

  • Author

    Zhang, Yilu ; Weng, Juyang ; Hwang, Wey-Shiuan

  • Author_Institution
    Res. Center, Gen. Motors Corp., Warren, MI, USA
  • Volume
    16
  • Issue
    3
  • fYear
    2005
  • fDate
    5/1/2005 12:00:00 AM
  • Firstpage
    601
  • Lastpage
    616
  • Abstract
    Motivated by the human autonomous development process from infancy to adulthood, we have built a robot that develops its cognitive and behavioral skills through real-time interactions with the environment. We call such a robot a developmental robot. In this paper, we present the theory and the architecture to implement a developmental robot and discuss the related techniques that address an array of challenging technical issues. As an application, experimental results on a real robot, self-organizing, autonomous, incremental learner (SAIL), are presented with emphasis on its audition perception and audition-related action generation. In particular, the SAIL robot conducts the auditory learning from unsegmented and unlabeled speech streams without any prior knowledge about the auditory signals, such as the designated language or the phoneme models. Neither available before learning starts are the actions that the robot is expected to perform. SAIL learns the auditory commands and the desired actions from physical contacts with the environment including the trainers.
  • Keywords
    cognitive systems; hearing; humanoid robots; learning (artificial intelligence); speech processing; SAIL robot; auditory learning; cognitive skill; developmental robot; human autonomous development process; unlabeled speech stream; unsegmented speech stream; Animals; Cognitive robotics; Context; Humans; Machine learning; Natural languages; Principal component analysis; Robots; Speech processing; Strontium; Classification; developmental learning; online auditory learning; principal component analysis (PCA); regression; reinforcement learning; Algorithms; Auditory Cortex; Auditory Perception; Computer Simulation; Humans; Learning; Models, Neurological; Nerve Net; Neural Networks (Computer); Pattern Recognition, Automated; Reinforcement (Psychology); Robotics; Speech Perception; Speech Recognition Software;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2005.845217
  • Filename
    1427765