Text-dependent Forensic Voice Comparison: Likelihood Ratio Estimation with the Hidden Markov Model (HMM) and Gaussian Mixture Model – Universal Background Model (GMMUBM) Approaches

Satoru Tsuge, Shunichi Ishihara

    Research output: Contribution to conferencePaper

    Abstract

    Among the more typical forensic voice comparison (FVC) approaches, the acoustic-phonetic statistical approach is suitable for text-dependent FVC, but it does not fully exploit available time-varying information of speech in its modelling. The automatic approach, on the other hand, essentially deals with text-independent cases, which means temporal information is not explicitly incorporated in the modelling. Text-dependent likelihood ratio (LR)-based FVC studies, in particular those that adopt the automatic approach, are few. This preliminary LR-based FVC study compares two statistical models, the Hidden Markov Model (HMM) and the Gaussian Mixture Model (GMM), for the calculation of forensic LRs using the same speech data. FVC experiments were carried out using different lengths of Japanese short words under a forensically realistic, but challenging condition: only two speech tokens for model training and LR estimation. Log-likelihood-ratio cost (Cllr) was used as the assessment metric. The study demonstrates that the HMM system constantly outperforms the GMM system in terms of average Cllr values. However, words longer than three mora are needed if the advantage of the HMM is to become evident. With a seven-mora word, for example, the HMM outperformed the GMM by a Cllr value of 0.073.
    Original languageEnglish
    Pages17-25
    Publication statusPublished - 2018
    Event16th Annual Workshop of The Australasian Language Technology Association (ALTA 2018) - Dunedin, New Zealand
    Duration: 1 Jan 2018 → …

    Conference

    Conference16th Annual Workshop of The Australasian Language Technology Association (ALTA 2018)
    Period1/01/18 → …

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