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Bio-inspired Spiking Ternary GRU achieves competitive speech recognition

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel Spiking Ternary GRU (stGRU) architecture inspired by the human cerebral cortex for improved speech recognition. This bio-inspired model couples parallel resonate-and-fire dynamics with ternary recurrent networks, enabling energy-efficient, event-driven computation. The stGRU achieves competitive Word Error Rates (WER) on the LibriSpeech-960 dataset and Character Error Rates (CER) on Aishell-1, notably without relying on external language models or dense floating-point operations. AI

IMPACT This research could lead to more energy-efficient and performant speech recognition systems by leveraging bio-inspired neural network designs.

RANK_REASON The cluster contains an academic paper detailing a new model architecture for speech recognition.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Bio-inspired Spiking Ternary GRU achieves competitive speech recognition

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The cluster contains an academic paper detailing a new model architecture for speech recognition.
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COVERAGE [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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