Inspired by the behaviour of humans talking
in noisy environments, we propose an embodied embedded
cognition approach to improve automatic speech recogni-
tion (ASR) systems for robots in complex situation, such
as with ego-noise, using binaural sound source localisation
(SSL). The approach is verified by measuring the impact
of SSL with a humanoid robot head on the performance
of an ASR system. More specifically, a robot orients itself
towards the angle where the signal-to-noise ratio (SNR)
of speech is maximised for one microphone before doing
an ASR task. First, a spiking neural network inspired by
the midbrain auditory system based on our previous work
is applied to calculate the sound signal angle. Then, a
feedforward neural network is used to handle high levels
of ego-noise and reverberation in the signal. Finally, the
sound signal is fed into an ASR system. For ASR, we
use a system developed by our group and compare its
performance with and without support from SSL. We test
our SSL and ASR systems on two humanoid platforms
with different structural and material properties. With our
approach we halve the sentence error rate with respect to
the common downmixing of both channels. Surprisingly,
the ASR performance is more than two times better when
the angle between the humanoid head and the sound source
allows sound waves to be reflected most intensely from the
pinna to the ear microphone, rather than when sound
waves arrive perpendicularly to the membrane.