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With the expansion of AI-powered virtual assistants, there is a need forlow-power keyword spotting systems providing a "wake-up" mechanism forsubsequent computationally expensive speech recognition. One promising approachis 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 streamingmanner, with physical time being an intrinsic property of their operation. Inthis work, two candidate neurocomputational elements for temporal encoding andfeature extraction in SNNs described in recent literature - the spikingtime-difference encoder (TDE) and disynaptic excitatory-inhibitory (E-I)elements - are comparatively investigated in a keyword-spotting task onformants computed from spoken digits in the TIDIGITS dataset. While bothencoders improve performance over direct classification of the formant featuresin the training data, enabling a complete binary classification with a logisticregression model, they show no clear improvements on the test set.Resource-efficient keyword spotting applications may benefit from the use ofthese encoders, but further work on methods for learning the time constants andweights is required to investigate their full potential.

A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons