  Artificial neural networks (ANN) have become the mainstream acoustic modeling
technique for large vocabulary automatic speech recognition (ASR). A
conventional ANN features a multi-layer architecture that requires massive
amounts of computation. The brain-inspired spiking neural networks (SNN)
closely mimic the biological neural networks and can operate on low-power
neuromorphic hardware with spike-based computation. Motivated by their
unprecedented energyefficiency and rapid information processing capability, we
explore the use of SNNs for speech recognition. In this work, we use SNNs for
acoustic modeling and evaluate their performance on several large vocabulary
recognition scenarios. The experimental results demonstrate competitive ASR
accuracies to their ANN counterparts, while require significantly reduced
computational cost and inference time. Integrating the algorithmic power of
deep SNNs with energy-efficient neuromorphic hardware, therefore, offer an
attractive solution for ASR applications running locally on mobile and embedded
devices.
