Many electronic devices spend most of their time waiting for a wake-up event:
pacemakers waiting for an anomalous heartbeat, security systems on alert to
detect an intruder, smartphones listening for the user to say a wake-up phrase.
These devices continuously convert physical signals into electrical currents
that are then analyzed on a digital computer -- leading to power consumption
even when no event is taking place. Solving this problem requires the ability
to passively distinguish relevant from irrelevant events (e.g. tell a wake-up
phrase from a regular conversation). Here, we experimentally demonstrate an
elastic metastructure, consisting of a network of coupled silicon resonators,
that passively discriminates between pairs of spoken words -- solving the
wake-up problem for scenarios where only two classes of events are possible.
This passive speech recognition is demonstrated on a dataset from speakers with
significant gender and accent diversity. The geometry of the metastructure is
determined during the design process, in which the network of resonators
('mechanical neurones') learns to selectively respond to spoken words. Training
is facilitated by a machine learning model that reduces the number of
computationally expensive three-dimensional elastic wave simulations. By
embedding event detection in the structural dynamics, mechanical neural
networks thus enable novel classes of always-on smart devices with no standby
power consumption.Comment: 13 pages, 9 figure