Speaker recognition using multiple kernel learning based on conditional entropy minimization

Tetsuji Ogawa, Hideitsu Hino, Nima Reyhani, Noboru Murata, Tetsunori Kobayashi

    研究成果: Conference contribution

    4 引用 (Scopus)

    抜粋

    We applied a multiple kernel learning (MKL) method based on information-theoretic optimization to speaker recognition. Most of the kernel methods applied to speaker recognition systems require a suitable kernel function and its parameters to be determined for a given data set. In contrast, MKL eliminates the need for strict determination of the kernel function and parameters by using a convex combination of element kernels. In the present paper, we describe an MKL algorithm based on conditional entropy minimization (MCEM). We experimentally verified the effectiveness of MCEM for speaker classification; this method reduced the speaker error rate as compared to conventional methods.

    元の言語English
    ホスト出版物のタイトルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    ページ2204-2207
    ページ数4
    DOI
    出版物ステータスPublished - 2011
    イベント36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague
    継続期間: 2011 5 222011 5 27

    Other

    Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
    Prague
    期間11/5/2211/5/27

      フィンガープリント

    ASJC Scopus subject areas

    • Signal Processing
    • Software
    • Electrical and Electronic Engineering

    これを引用

    Ogawa, T., Hino, H., Reyhani, N., Murata, N., & Kobayashi, T. (2011). Speaker recognition using multiple kernel learning based on conditional entropy minimization. : ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 2204-2207). [5946918] https://doi.org/10.1109/ICASSP.2011.5946918