### Abstract

Two procedures for the calculation of forensic likelihood ratios were tested on the same set of acoustic-phonetic data. One procedure was a multivariate kernel density procedure (MVKD) which is common in acoustic-phonetic forensic voice comparison, and the other was a Gaussian mixture model-universal background model (GMM-UBM) which is common in automatic forensic voice comparison. The data were coefficient values from discrete cosine transforms fitted to second-formant trajectories of /a/, /e/, /o/, /a/, and // tokens produced by 27 male speakers of Australian English. Scores were calculated separately for each phoneme and then fused using logistic regression. The performance of the fused GMM-UBM system was much better than that of the fused MVKD system, both in terms of accuracy (as measured using the log-likelihood-ratio cost, C_{llr}) and precision (as measured using an empirical estimate of the 95% credible interval for the likelihood ratios from the different-speaker comparisons).

Original language | English |
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Pages (from-to) | 242-256 |

Number of pages | 15 |

Journal | Speech Communication |

Volume | 53 |

Issue number | 2 |

DOIs | |

Publication status | Published - Feb 2011 |

### Keywords

- Acoustic-phonetic
- Forensic voice comparison
- GMM-UBM
- Likelihood ratio
- Multivariate kernel density

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## Cite this

*Speech Communication*,

*53*(2), 242-256. https://doi.org/10.1016/j.specom.2010.09.005