Adaptive Speech-to-Spike Encoding for Spiking Neural Networks
Researchers have developed a novel learnable residual speech-to-spike encoder designed to improve neuromorphic speech processing. This encoder is trained end-to-end with a Recurrent Leaky Integrate-and-Fire backbone, addressing the fundamental mismatch between continuous acoustic signals and discrete event-driven processing in Spiking Neural Networks (SNNs). The approach achieved up to 94.97% accuracy on the Google Speech Commands v2 benchmark, with a parameter-efficient variant reaching 89.8%. Analysis suggests the encoder learns task-aligned spike representations rather than direct signal reconstruction, enhancing class separability. AI
IMPACT Enhances efficiency and accuracy in neuromorphic speech processing systems.