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

Citation

Tsuge, S & Ishihara, S 2018, 'Text-dependent Forensic Voice Comparison: Likelihood Ratio Estimation with the Hidden Markov Model (HMM) and Gaussian Mixture Model - Universal Background Model (GMM-UBM) Approaches.', Proceedings of the Australasian Language Technology Association Workshop 2018, ed. Sunghwan Mac Kim and Xiuzhen (Jenny) Zhang, The Australasian Language Technology Association workshop, Dunedin, New Zealand, pp. 17-25.

Year

2018

Fields of Research

  • Laboratory Phonetics And Speech Science
  • Language Studies

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