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

受生物启发的脉冲三元GRU在语音识别方面表现出色

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的脉冲三元GRU（stGRU）架构，该架构受到人脑皮层的启发，用于改进语音识别。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，实现了能效高、事件驱动的计算。stGRU在LibriSpeech-960数据集上实现了具有竞争力的词错误率（WER），在Aishell-1数据集上实现了字符错误率（CER），尤其值得注意的是，它不依赖于外部语言模型或密集的浮点运算。 AI

影响 这项研究通过利用受生物启发的神经网络设计，有望带来更节能、性能更优的语音识别系统。

排序理由 该集群包含一篇详细介绍用于语音识别的新模型架构的学术论文。

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

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受生物启发的脉冲三元GRU在语音识别方面表现出色

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Pavlos Poulos, Katelyn Sadorf, Themos Stafylakis, Costas A. Anastassiou ·

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

    arXiv:2610.29720v1 Announce Type: cross Abstract: 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…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Costas A. Anastassiou ·

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

    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…

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