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

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

    4 Citations (Scopus)


    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.
    Number of pages5
    ISBN (Print)9781467369978
    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


    Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015


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

    ASJC Scopus subject areas

    • Signal Processing
    • Software
    • Electrical and Electronic Engineering

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