Replicate Mismatch between Test and Background/Development Databases: The Effect on the Performance of Likelihood Ratio-based Forensic Voice Comparison

In this paper, we report on a study which demonstrates that the mismatch in within-speaker replicate numbers (the number of tokens used to model each sample) between test/background and development databases has a large impact on the performance of a forensic voice comparison (FVC) system. We describe how and to what extent the different degrees of the mismatch influence the performance of the FVC system. The performance of an FVC system based on temporal MFCC features and the Multivariate Kernel Density Likelihood Ratio procedure is tested in terms of its validity and reliability under the mismatched conditions. The Monte Carlo technique is employed to repeatedly carry out FVC tests. We report that the databases matched with respect to replicate numbers result in optimal performance in terms of validity, but not in terms of reliability.