Sound source separation for robot audition using deep learning

Kuniaki Noda, Naoya Hashimoto, Kazuhiro Nakadai, Tetsuya Ogata

    研究成果: Conference contribution

    10 被引用数 (Scopus)


    Noise robust speech recognition is crucial for effective human-machine interaction in real-world environments. Sound source separation (SSS) is one of the most widely used approaches for addressing noise robust speech recognition by extracting a target speaker's speech signal while suppressing simultaneous unintended signals. However, conventional SSS algorithms, such as independent component analysis or nonlinear principal component analysis, are limited in modeling complex projections with scalability. Moreover, conventional systems required designing an independent subsystem for noise reduction (NR) in addition to the SSS. To overcome these issues, we propose a deep neural network (DNN) framework for modeling the separation function (SF) of an SSS system. By training a DNN to predict clean sound features of a target sound from corresponding multichannel deteriorated sound feature inputs, we enable the DNN to model the SF for extracting the target sound without prior knowledge regarding the acoustic properties of the surrounding environment. Moreover, the same DNN is trained to function simultaneously as a NR filter. Our proposed SSS system is evaluated using an isolated word recognition task and a large vocabulary continuous speech recognition task when either nondirectional or directional noise is accumulated in the target speech. Our evaluation results demonstrate that DNN performs noticeably better than the baseline approach, especially when directional noise is accumulated with a low signal-to-noise ratio.

    ホスト出版物のタイトルIEEE-RAS International Conference on Humanoid Robots
    出版社IEEE Computer Society
    出版ステータスPublished - 2015 12月 22
    イベント15th IEEE RAS International Conference on Humanoid Robots, Humanoids 2015 - Seoul, Korea, Republic of
    継続期間: 2015 11月 32015 11月 5


    Other15th IEEE RAS International Conference on Humanoid Robots, Humanoids 2015
    国/地域Korea, Republic of

    ASJC Scopus subject areas

    • 人工知能
    • コンピュータ ビジョンおよびパターン認識
    • ハードウェアとアーキテクチャ
    • 人間とコンピュータの相互作用
    • 電子工学および電気工学


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