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Different variants of a Forensic Automatic Speaker Recognition (FASR) systembased on Emphasized Channel Attention, Propagation and Aggregation in TimeDelay Neural Network (ECAPA-TDNN) are tested under conditions reflecting thoseof a real forensic voice comparison case, according to the forensic_eval_01evaluation campaign settings. Using this recent neural model as an embeddingextraction block, various normalization strategies at the level of embeddingsand scores allow us to observe the variations in system performance, in termsof discriminating power, accuracy and precision metrics. From the achievedresults it is possible to state that ECAPA-TDNN can be very successfully usedas a base component of a FASR system, managing to surpass the previous state ofthe art, at least in the context of the considered operating conditions.

Validation of an ECAPA-TDNN system for Forensic Automatic Speaker Recognition under case work conditions