# Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition

arXiv: https://arxiv.org/abs/1911.08373 · HTML: https://mlsanity.com/paper/1911.08373/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 27 of 27)

- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [Advancing Spiking Neural Networks towards Deep Residual Learning](https://mlsanity.com/paper/2112.08954/) (2021)
- [Direct Learning-Based Deep Spiking Neural Networks: A Review](https://mlsanity.com/paper/2305.19725/) (2023)
- [Human-Level Control through Directly-Trained Deep Spiking Q-Networks](https://mlsanity.com/paper/2201.07211/) (2021)
- [EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2403.12574/) (2024)
- [Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning](https://mlsanity.com/paper/2407.08130/) (2024)
- [SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2404.07941/) (2024)
- [Brain-Inspired Online Adaptation for Remote Sensing with Spiking Neural Network](https://mlsanity.com/paper/2409.02146/) (2024)
- [Spiking Variational Graph Representation Inference for Video Summarization](https://mlsanity.com/paper/2508.15389/) (2025)
- [Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks](https://mlsanity.com/paper/2106.00687/) (2021)
- [Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks](https://mlsanity.com/paper/2201.07209/) (2021)
- [Surrogate Gradient Spiking Neural Networks as Encoders for Large Vocabulary Continuous Speech Recognition](https://mlsanity.com/paper/2212.01187/) (2022)
- [SpikeVoice: High-Quality Text-to-Speech Via Efficient Spiking Neural Network](https://mlsanity.com/paper/2408.00788/) (2024)
- [SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection](https://mlsanity.com/paper/2311.06570/) (2023)
- [Watermarking Neuromorphic Brains: Intellectual Property Protection in Spiking Neural Networks](https://mlsanity.com/paper/2405.04049/) (2024)
- [Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks](https://mlsanity.com/paper/2502.05509/) (2025)
- [A 71.2-μW Speech Recognition Accelerator with Recurrent Spiking Neural Network](https://mlsanity.com/paper/2503.21337/) (2025)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [Efficient Training of Spiking Neural Networks by Spike-aware Data Pruning](https://mlsanity.com/paper/2510.04098/) (2025)
- [PTS-SNN: A Prompt-Tuned Temporal Shift Spiking Neural Networks for Efficient Speech Emotion Recognition](https://mlsanity.com/paper/2602.08240/) (2026)
- [Deep activity propagation via weight initialization in spiking neural networks](https://mlsanity.com/paper/2410.00580/) (2024)
- [The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs](https://mlsanity.com/paper/2506.19642/) (2025)
- [Practical Bayesian Inference for Speech SNNs: Uncertainty and Loss-Landscape Smoothing](https://mlsanity.com/paper/2604.08624/) (2026)
- [QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting](https://mlsanity.com/paper/2605.18333/) (2026)
- [Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networks](https://mlsanity.com/paper/2606.23761/) (2026)
- [End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing](https://mlsanity.com/paper/2606.24075/) (2026)
- [SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting](https://mlsanity.com/paper/2609.35097/) (2026)

## Abstract (arXiv)

Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi-layer architecture that requires massive amounts of computation. The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation. Motivated by their unprecedented energyefficiency and rapid information processing capability, we explore the use of SNNs for speech recognition. In this work, we use SNNs for acoustic modeling and evaluate their performance on several large vocabulary recognition scenarios. The experimental results demonstrate competitive ASR accuracies to their ANN counterparts, while require significantly reduced computational cost and inference time. Integrating the algorithmic power of deep SNNs with energy-efficient neuromorphic hardware, therefore, offer an attractive solution for ASR applications running locally on mobile and embedded devices.