  Recurrent neural networks (RNNs) have shown promising results in audio and
speech processing applications due to their strong capabilities in modelling
sequential data. In many applications, RNNs tend to outperform conventional
models based on GMM/UBMs and i-vectors. Increasing popularity of IoT devices
makes a strong case for implementing RNN based inferences for applications such
as acoustics based authentication, voice commands, and edge analytics for smart
homes. Nonetheless, the feasibility and performance of RNN based inferences on
resources-constrained IoT devices remain largely unexplored. In this paper, we
investigate the feasibility of using RNNs for an end-to-end authentication
system based on breathing acoustics. We evaluate the performance of RNN models
on three types of devices; smartphone, smartwatch, and Raspberry Pi and show
that unlike CNN models, RNN models can be easily ported onto
resource-constrained devices without a significant loss in accuracy.
