LR-based forensic comparison under severe test-data scarcity

Yuko Kinoshita, Michael Wagner

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    This study sets out to find the most reliable method for loglikelihood-ratio (LLR) calculation under severe data scarcity, which is typical of forensic voice comparison casework. We compared the performances of three types of speaker modelling, namely a single Gaussian model, Gaussian Mixture Models (GMM) of different complexity, and a Multivariate Kernel Density Model (MVKD), using two and threedimensional formant frequency feature vectors extracted from /iː/ vowels. We varied the number of tokens used in the offender dataset from 2 to 6. We find that calibration of the systems was critical for dependable evaluation with all the systems tested and that the MVKD model outperformed Gaussian models in most cases.
    Original languageEnglish
    Title of host publicationInterpeech 2014
    Place of PublicationSingapore
    PublisherInternational Speech Communication Association
    Pages16-19
    EditionPeer Reviewed
    Publication statusPublished - 2014
    EventAnnual Conference of the International Speech Communication Association INTERSPEECH 2014 - Singapore, Singapore
    Duration: 1 Jan 2014 → …
    http://www.isca-speech.org/archive/interspeech_2014

    Conference

    ConferenceAnnual Conference of the International Speech Communication Association INTERSPEECH 2014
    Country/TerritorySingapore
    Period1/01/14 → …
    OtherSeptember 14-18 2014
    Internet address

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