A comparative study of spectral clustering for i-vector-based speaker clustering under noisy conditions

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    The present paper dealt with speaker clustering for speech corrupted by noise. In general, the performance of speaker clustering significantly depends on how well the similarities between speech utterances can be measured. The recently proposed i-vector-based cosine similarity has yielded the state-of-the-art performance in speaker clustering systems. However, this similarity often fails to capture the speaker similarity under noisy conditions. Therefore, we attempted to examine the efficiency of spectral clustering on i-vector-based similarity for speech corrupted by noise because spectral clustering can yield robustness against noise by non-linear projection. Experimental comparisons demonstrated that spectral clustering yielded significant improvement from conventional methods, such as agglomerative clustering and k-means clustering, under non-stationary noise conditions.

    Original languageEnglish
    Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2041-2045
    Number of pages5
    Volume2015-August
    ISBN (Print)9781467369978
    DOIs
    Publication statusPublished - 2015 Aug 4
    Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
    Duration: 2014 Apr 192014 Apr 24

    Other

    Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
    CountryAustralia
    CityBrisbane
    Period14/4/1914/4/24

    Keywords

    • i-vector
    • noise-robust speaker clustering
    • spectral clustering

    ASJC Scopus subject areas

    • Signal Processing
    • Software
    • Electrical and Electronic Engineering

    Cite this

    Tawara, N., Ogawa, T., & Kobayashi, T. (2015). A comparative study of spectral clustering for i-vector-based speaker clustering under noisy conditions. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 2015-August, pp. 2041-2045). [7178329] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2015.7178329

    A comparative study of spectral clustering for i-vector-based speaker clustering under noisy conditions. / Tawara, Naohiro; Ogawa, Tetsuji; Kobayashi, Tetsunori.

    ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 2015-August Institute of Electrical and Electronics Engineers Inc., 2015. p. 2041-2045 7178329.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Tawara, N, Ogawa, T & Kobayashi, T 2015, A comparative study of spectral clustering for i-vector-based speaker clustering under noisy conditions. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. vol. 2015-August, 7178329, Institute of Electrical and Electronics Engineers Inc., pp. 2041-2045, 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015, Brisbane, Australia, 14/4/19. https://doi.org/10.1109/ICASSP.2015.7178329
    Tawara N, Ogawa T, Kobayashi T. A comparative study of spectral clustering for i-vector-based speaker clustering under noisy conditions. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 2015-August. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2041-2045. 7178329 https://doi.org/10.1109/ICASSP.2015.7178329
    Tawara, Naohiro ; Ogawa, Tetsuji ; Kobayashi, Tetsunori. / A comparative study of spectral clustering for i-vector-based speaker clustering under noisy conditions. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 2015-August Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2041-2045
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    abstract = "The present paper dealt with speaker clustering for speech corrupted by noise. In general, the performance of speaker clustering significantly depends on how well the similarities between speech utterances can be measured. The recently proposed i-vector-based cosine similarity has yielded the state-of-the-art performance in speaker clustering systems. However, this similarity often fails to capture the speaker similarity under noisy conditions. Therefore, we attempted to examine the efficiency of spectral clustering on i-vector-based similarity for speech corrupted by noise because spectral clustering can yield robustness against noise by non-linear projection. Experimental comparisons demonstrated that spectral clustering yielded significant improvement from conventional methods, such as agglomerative clustering and k-means clustering, under non-stationary noise conditions.",
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