The effect of variability on the outcome of likelihood ratios

The likelihood ratio (LR) is the “logically and legally correct” (Rose and Morrison 2009:143) framework for the estimation of strength-of-evidence under two competing hypotheses. In forensic voice comparison these considerations are reduced to the similarity and typicality of features across a pair of suspect and offender samples. However, typicality can only be judged against patterns in the relevant population (Aitken and Taroni 2004:206). In calculating numerical LRs typicality is assessed relative to a sub-section of that population. This study considers issues of variability relating to the delimitation of reference data with regard to the number of speakers and number of tokens per speaker. Using polynomial estimations of F1 and F2 trajectories from spontaneous GOOSE (Wells 1982), LR comparisons were performed against a reference set of up to 120 speakers and up to 13 tokens per speaker. Results suggest that mean same-speaker LRs are robust to such variation until the reference data is limited to small numbers of speakers and tokens. However, variance and severity of error may be continually reduced with the inclusion of more data. The definition of the relevant population with regard to regional variety is also assessed. Results for LRs are presented across four sets of test data where only one set matches the reference population for accent. In the absence of differences in levels of within-speaker variation, the magnitude of same-speaker LRs and severity of error are shown to be considerably higher for the ‘mismatch’ test sets. However, results indicate that the removal of regionally-defining acoustic information may reduce the effect of accent divergence between the evidential and reference data. This has positive implications for the application of the numerical LR approach.