Quantitative influence of speech variability factors for automatic speaker verification in forensic tasks

Regarding speaker identity in forensic conditions, several factors of variability must be taken into account, as peculiar intra-speaker variability, forced intra-speaker variability or channel-dependent external influences. Using ‘AHUMADA’ large speech database in Spanish, containing several recording sessions and channels, and including different tasks for 100 male speakers, automatic speaker verification experiments are accomplished. Due to the inherent non-cooperative nature of speakers in forensic applications, only text-independent recognizers are likely to be used. In this sense, a GMM-based verification system has been used in order to obtain quantitative results. Maximum likelihood estimation of the models is performed, and LPC-cepstra, deltaand delta-delta-LPCC, are used at the parameterization stage. With this baseline verification system, we intend to determine how some variability sources included in ‘AHUMADA’ affect speaker identification. Results including speaking rate influence, singleand multi-session training and cross-channel testing are presented when likelihood-domain normalization is applied.