End-to-End Spiking Neural Network for Speech Recognition Using Resonating Input Neurons

The growing demand for complex computations in edge devices requires the development of algorithms and hardware accelerators that are powerful while remaining energy-efficient. A possible solution are spiking neural networks, as they have been demonstrated to be energy-efficient in several data processing and classification tasks when executed on specialized neuromorphic hardware. In the field of speech processing, they are especially suited for the online classification of audio streams due to their strong temporal affinity. However, so far, there has been a lack of emphasis on small-scale networks that will ultimately fit into restricted neuromorphic implementations. We propose the use of resonating neurons as an input layer to spiking neural networks for online audio classification to enable an end-to-end solution. We compare different architectures to the established method of using mel-frequency-based spectral features. With our approach, spiking neural networks can be directly used without additional preprocessing, thereby making them suitable for simple continuous low-power analysis of audio streams. We compare the classification accuracy of different network architectures with ours in a keyword spotting benchmark to demonstrate the performance of our approach.