  Speaker Recognition and Speaker Identification are challenging tasks with
essential applications such as automation, authentication, and security. Deep
learning approaches like SincNet and AM-SincNet presented great results on
these tasks. The promising performance took these models to real-world
applications that becoming fundamentally end-user driven and mostly mobile. The
mobile computation requires applications with reduced storage size,
non-processing and memory intensive and efficient energy-consuming. The deep
learning approaches, in contrast, usually are energy expensive, demanding
storage, processing power, and memory. To address this demand, we propose a
portable model called Additive Margin MobileNet1D (AM-MobileNet1D) to Speaker
Identification on mobile devices. We evaluated the proposed approach on TIMIT
and MIT datasets obtaining equivalent or better performances concerning the
baseline methods. Additionally, the proposed model takes only 11.6 megabytes on
disk storage against 91.2 from SincNet and AM-SincNet architectures, making the
model seven times faster, with eight times fewer parameters.

Sound;Computation and Language;Machine Learning;Audio and Speech Processing