Prof. Alexander Wong's work on TinySpeech, a family of compact deep neural networks for on-device speech recognition, was featured in the media, including in Synced and Hackster.io:
Advances
in
natural
language
processing
(NLP)
driven
by
the
BERT
language
model
and
transformer
models
have
produced
SOTA
performances
in
tasks
such
as
speech
recognition
and
powered
a
range
of
applications
including
voice
assistants
and
real-time
closed
captioning.
The
widespread
deployment
of
deep
neural
networks
for
on-device
speech
recognition
however
remains
a
challenge,
particularly
on
edge
devices
such
as
mobile
phones.
In
a
new
paper,
researchers
from
the
University
of
Waterloo
and
DarwinAI
propose
novel
attention
condensers
designed
to
enable
the
building
of
low-footprint,
highly-efficient
deep
neural
networks
for
on-device
speech
recognition
on
the
edge.
The
team
demonstrates
low-precision
“TinySpeech”
deep
neural
networks
comprising
such
attention
condensers
and
tailored
specifically
for
limited-vocabulary
speech
recognition.
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