  Speech recognition is a critical task in the field of artificial intelligence
and has witnessed remarkable advancements thanks to large and complex neural
networks, whose training process typically requires massive amounts of labeled
data and computationally intensive operations. An alternative paradigm,
reservoir computing, is energy efficient and is well adapted to implementation
in physical substrates, but exhibits limitations in performance when compared
to more resource-intensive machine learning algorithms. In this work we address
this challenge by investigating different architectures of interconnected
reservoirs, all falling under the umbrella of deep reservoir computing. We
propose a photonic-based deep reservoir computer and evaluate its effectiveness
on different speech recognition tasks. We show specific design choices that aim
to simplify the practical implementation of a reservoir computer while
simultaneously achieving high-speed processing of high-dimensional audio
signals. Overall, with the present work we hope to help the advancement of
low-power and high-performance neuromorphic hardware.
