  Deep neural network approaches to speaker verification have proven
successful, but typical computational requirements of State-Of-The-Art (SOTA)
systems make them unsuited for embedded applications. In this work, we present
a two-stage model architecture orders of magnitude smaller than common
solutions (237.5K learning parameters, 11.5MFLOPS) reaching a competitive
result of 3.31% Equal Error Rate (EER) on the well established VoxCeleb1
verification test set. We demonstrate the possibility of running our solution
on small devices typical of IoT systems such as the Raspberry Pi 3B with a
latency smaller than 200ms on a 5s long utterance. Additionally, we evaluate
our model on the acoustically challenging VOiCES corpus. We report a limited
increase in EER of 2.6 percentage points with respect to the best scoring model
of the 2019 VOiCES from a Distance Challenge, against a reduction of 25.6 times
in the number of learning parameters.
