  Brain-inspired spiking neural networks (SNNs) have demonstrated great
potential for temporal signal processing. However, their performance in speech
processing remains limited due to the lack of an effective auditory front-end.
To address this limitation, we introduce Spiking-LEAF, a learnable auditory
front-end meticulously designed for SNN-based speech processing. Spiking-LEAF
combines a learnable filter bank with a novel two-compartment spiking neuron
model called IHC-LIF. The IHC-LIF neurons draw inspiration from the structure
of inner hair cells (IHC) and they leverage segregated dendritic and somatic
compartments to effectively capture multi-scale temporal dynamics of speech
signals. Additionally, the IHC-LIF neurons incorporate the lateral feedback
mechanism along with spike regularization loss to enhance spike encoding
efficiency. On keyword spotting and speaker identification tasks, the proposed
Spiking-LEAF outperforms both SOTA spiking auditory front-ends and conventional
real-valued acoustic features in terms of classification accuracy, noise
robustness, and encoding efficiency.
