In this thesis an ASIC design of an hardware GMM-Based Speaker Classifier is presented. The Classifier is a fundamental component of a Speaker Identification system, that is able to associate an unknown incoming speech signal to its unknown speaker, which is part of a previously modeled group of speakers. The design flow fol- lows a software-to-hardware approach, since the whole system is firstly implemented in Matlab, then increasingly transformed from high-level to machine-level until its hardware description. Innovative techniques that avoid any memory accesses to per- form hardware exponentials and logarithms are presented. All the computations are executed on-chip and this gives extra performances and extra security to the system.