The Institute of Scientific and Industrial Research, Osaka University


LAST UPDATE 2017/02/26

  • 研究者氏名
    Researcher Name

    武田龍 Ryu TAKEDA
    助教 Assistant Professor
  • 所属
    Professional Affiliation

    The Institute of Scientific and Industrial Research, Osaka University

    Department of Knowledge Science
  • 研究キーワード
    Research Keywords

    Acoustic signal processing
    Automatic speech recognition
    Spoken dialogue
Research Subject
Automatic improvement of acoustic and language models for automatic speech recognition through spoken dialogues

研究の背景 Background


Research and development of intelligent systems and robots have recently advanced, and they start appearing in our society. For example, humanoid robots such as Nao and Pepper can recognize human speech and emotions, and thus they can interact with us using speech. The importance, necessity, and societal expectation of speech as an interface will increase according to such a rapid development of robots.

研究の目標 Outcome


The operation of intelligent system that can talk with us requires recognizing a variety of human speech signals and varying words correctly at all times. The maintenance of such systems is usually performed by updating acoustic and language models of automatic speech recognition (ASR) after system developers detect misrecognized utterances from the system logs. The purpose of our research is to realize a maintenance-free spoken dialogue system regarding the ASR models. We also study techniques of audio signal processing and ASR required when implementing spoken dialogue robots.

研究図Research Figure

Fig.1. A scheme of maintenance-free spoken dialogue system. Fig.2. Preprocessing including sound source separation and automatic speech recognition before dialogue management. Fig.3. Blind source separation of mixture of two speech signals in highly reverberant environment.

文献 / Publications

Ryu Takeda, et al., “Boundary Contraction Training for Acoustic Models based on Discrete Deep Neural Networks,” Proceedings of Interspeech, pp.1063-1067, 2014
Ryu Takeda, et al., “Efficient Blind Dereverberation and Echo Cancellation based on Independent Component Analysis for Actual Acoustic Signals,” Neural Computation, Vol.24, Issue 1, pp.234-272, 2012