arXiv:2606.19039v1 Announce Type: cross Abstract: The mismatch between continuous acoustic signals and discrete event-driven processing remains a fundamental bottleneck for neuromorphic speech processing. Current systems typically rely on fixed spike encoders, forcing downstream Spiking Neural Networks (SNNs) to compensate for non-adaptive input representations. To address this, we present a learnable residual speech-to-spike encoder jointly trained end-to-end with a Recurrent Leaky Integrate-and-Fire (R-LIF) backbone. We validate this approach on the Google Speech Commands v2 (GSC-v2) benchma
Ongoing research into more efficient and biologically inspired AI architectures for specific modalities like speech processing continues to push for better integration between input and SNNs.
This development addresses a fundamental efficiency bottleneck in neuromorphic speech processing, potentially leading to more power-efficient and faster AI applications in edge devices.
The ability to jointly train adaptive spike encoders with SNNs optimizes the input representation for event-driven systems, moving away from static, suboptimal preprocessing.
- · Neuromorphic hardware manufacturers
- · Edge AI developers
- · Speech recognition technology providers
- · Traditional fixed-encoder speech processing systems
Improved accuracy and efficiency for speech-based applications running on spiking neural networks.
Accelerated adoption of neuromorphic computing for real-time, low-power speech and audio processing tasks.
Expansion of SNNs into broader multimodal AI applications beyond speech, leveraging adaptive encoding principles.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG