  Different variants of a Forensic Automatic Speaker Recognition (FASR) system
based on Emphasized Channel Attention, Propagation and Aggregation in Time
Delay Neural Network (ECAPA-TDNN) are tested under conditions reflecting those
of a real forensic voice comparison case, according to the forensic_eval_01
evaluation campaign settings. Using this recent neural model as an embedding
extraction block, various normalization strategies at the level of embeddings
and scores allow us to observe the variations in system performance, in terms
of discriminating power, accuracy and precision metrics. From the achieved
results it is possible to state that ECAPA-TDNN can be very successfully used
as a base component of a FASR system, managing to surpass the previous state of
the art, at least in the context of the considered operating conditions.
