### Abstract

as a function of the size of the reference data. Results reveal that LRs based on AR are relatively robust to small reference samples, but that system calibration plays an important role in determining the sensitivity of the LRs to sample size.

Original language | English |
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Journal | York Papers in Linguistics |

Issue number | 13 |

Publication status | Published - Dec 2013 |

Externally published | Yes |

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### Cite this

*York Papers in Linguistics*, (13).

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*York Papers in Linguistics*, no. 13.

**Reference Sample Size and the Computation of Numerical Likelihood Ratios Using Articulation Rate.** / Hughes, Vincent; Brereton, Ashley; Gold, Erica.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Reference Sample Size and the Computation of Numerical Likelihood Ratios Using Articulation Rate

AU - Hughes, Vincent

AU - Brereton, Ashley

AU - Gold, Erica

PY - 2013/12

Y1 - 2013/12

N2 - This paper explores the effects of variability in the amount of reference data used in quantifying the strength of speech evidence using numerical likelihood ratios (LRs). Monte Carlo simulations (MCS) are performed to generate synthetic data from a sample of existing raw local articulation rate (AR) data. LRs are computed as the number of reference speakers (up to 1000), and the number of tokens per reference speaker (up to 200) is systematically increased. The distributions of same-speaker and different-speaker LRs and system performance (log LR cost (Cllr) and equal error rate (EER)) are assessedas a function of the size of the reference data. Results reveal that LRs based on AR are relatively robust to small reference samples, but that system calibration plays an important role in determining the sensitivity of the LRs to sample size.

AB - This paper explores the effects of variability in the amount of reference data used in quantifying the strength of speech evidence using numerical likelihood ratios (LRs). Monte Carlo simulations (MCS) are performed to generate synthetic data from a sample of existing raw local articulation rate (AR) data. LRs are computed as the number of reference speakers (up to 1000), and the number of tokens per reference speaker (up to 200) is systematically increased. The distributions of same-speaker and different-speaker LRs and system performance (log LR cost (Cllr) and equal error rate (EER)) are assessedas a function of the size of the reference data. Results reveal that LRs based on AR are relatively robust to small reference samples, but that system calibration plays an important role in determining the sensitivity of the LRs to sample size.

M3 - Article

JO - York Papers in Linguistics

JF - York Papers in Linguistics

SN - 1758-0315

IS - 13

ER -