A significant question for forensic voice comparison, and for speaker recognition more generally, is the extent to which different input features capture complementary speakerspecific information. Understanding complementarity allows us to make predictions about how combining methods using different features may produce better overall performance. In forensic contexts, it is also important to be able to explain to courts what information the underlying features are actually capturing. This

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... addresses these issues by examining the extent to which MFCCs and LPCCs can predict F0, F1, F2, and F3 values using data extracted from the midpoint of the vocalic portion of the hesitation marker um for 89 speakers of standard southern British English. By-speaker correlations were calculated using multiple linear regression and performance was assessed using mean rho ( ) values. Results show that the first two formants were more accurately predicted than F3 or F0. LPCCs consistently produced stronger correlations with the linguistic features than MFCCs, while increasing cepstral order up to 16 also increased the strength of the correlations. There was, however, considerable variability across speakers in terms of the accuracy of the predictions. We discuss the implications of these findings for forensic voice comparison.