English(EN) Bio-Inspired Auditory Processing: Coupling Parallel Resonate-and-Fire Dynamics with Ternary Recurrent Networks for Speech Recognition

受生物启发的脉冲网络在语音识别上实现个位数词错误率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 13:29

研究人员开发了一种新颖的脉冲三元GRU（stGRU）架构，提高了自动语音识别（ASR）的能效和性能。这种受生物启发的模型将并行共振激发动力学与三元循环网络相结合，解决了传统漏积分发放神经元的局限性。stGRU在LibriSpeech-960数据集上实现了具有竞争力的词错误率，标志着脉冲神经网络在复杂、连续任务上取得了重大进展。 AI

影响引入了一种更节能的语音识别架构，可能影响未来的ASR系统设计。

排序理由学术论文，详细介绍了新颖的神经网络架构及其在基准数据集上的性能。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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报道来源 [1]

arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Costas A. Anastassiou · 2026-10-21 13:29

仿生听觉处理：结合并行共振激发动力学与三元循环网络用于语音识别

Spiking Neural Networks (SNNs) promise energy-efficient inference through sparse, event-driven computation, yet remain constrained on continuous, context-dependent tasks such as automatic speech recognition (ASR). A key bottleneck is the Leaky Integrate-and-Fire (LIF) neuron, who…

本文如何被排名

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仿生听觉处理：结合并行共振激发动力学与三元循环网络用于语音识别

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