  With the expansion of AI-powered virtual assistants, there is a need for
low-power keyword spotting systems providing a "wake-up" mechanism for
subsequent computationally expensive speech recognition. One promising approach
is the use of neuromorphic sensors and spiking neural networks (SNNs)
implemented in neuromorphic processors for sparse event-driven sensing.
However, this requires resource-efficient SNN mechanisms for temporal encoding,
which need to consider that these systems process information in a streaming
manner, with physical time being an intrinsic property of their operation. In
this work, two candidate neurocomputational elements for temporal encoding and
feature extraction in SNNs described in recent literature - the spiking
time-difference encoder (TDE) and disynaptic excitatory-inhibitory (E-I)
elements - are comparatively investigated in a keyword-spotting task on
formants computed from spoken digits in the TIDIGITS dataset. While both
encoders improve performance over direct classification of the formant features
in the training data, enabling a complete binary classification with a logistic
regression model, they show no clear improvements on the test set.
Resource-efficient keyword spotting applications may benefit from the use of
these encoders, but further work on methods for learning the time constants and
weights is required to investigate their full potential.
