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The spiking neural network (SNN) using leaky-integrated-and-fire (LIF)neurons has been commonly used in automatic speech recognition (ASR) tasks.However, the LIF neuron is still relatively simple compared to that in thebiological brain. Further research on more types of neurons with differentscales of neuronal dynamics is necessary. Here we introduce four types ofneuronal dynamics to post-process the sequential patterns generated from thespiking transformer to get the complex dynamic neuron improved spikingtransformer neural network (DyTr-SNN). We found that the DyTr-SNN could handlethe non-toy automatic speech recognition task well, representing a lowerphoneme error rate, lower computational cost, and higher robustness. Theseresults indicate that the further cooperation of SNNs and neural dynamics atthe neuron and network scales might have much in store for the future,especially on the ASR tasks.

Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition