Speech2Spikes: Efficient Audio Encoding Pipeline for Real-time Neuromorphic Systems

Despite the maturity and availability of speech recognition systems, there are few available spiking speech recognition tasks that can be implemented with current neuromorphic systems. The methods used previously to generate spiking speech data are not capable of encoding speech in real-time or encoding very large modern speech datasets efficiently for input to neuromorphic processors. The ability to efficiently encode audio data to spikes will enable a wider variety of spiking audio datasets to be available and can also enable algorithmic development of real-time neuromorphic automatic speech recognition systems. Therefore, we developed speech2spikes, a simple and efficient audio processing pipeline that encodes recorded audio into spikes and is suitable for real-time operation with low-power neuromorphic processors. To demonstrate the efficacy of our method for audio to spike encoding we show that a small feed-forward spiking neural network trained on data generated with the pipeline achieves 88.5 +/- 0.1% accuracy on the Google Speech Commands recognition task, exceeding the state-of-the art set by Spiking Speech Commands, a prior spiking encoding of the Google Speech Commands dataset, by over 10%. We also demonstrate a proof-of-concept real-time neuromorphic automatic speech recognition system using audio encoded with speech2spikes streamed to an Intel Loihi neuromorphic research processor.