ArticleFrontiers in neuroscience2020
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Speech Recognition with an fMRISNN Constrained by Human Functional Brain Networks: A Study of Enhanced MFCC-Driven Sparse Spike Encoding.Biomimetics (Basel, Switzerland) · 2026Article
- Motion feature extraction using magnocellular-inspired spiking neural networks for drone detection.Frontiers in computational neuroscience · 2025Article
- Snn and sound: a comprehensive review of spiking neural networks in sound.Biomedical engineering letters · 2024Review
- Adaptive spatiotemporal neural networks through complementary hybridization.Nature communications · 2024Article
- Exploring the performance of automatic speaker recognition using twin speech and deep learning-based artificial neural networks.Frontiers in artificial intelligence · 2024Article
- Learnable axonal delay in spiking neural networks improves spoken word recognition.Frontiers in neuroscience · 2023Article
- Direct learning-based deep spiking neural networks: a review.Frontiers in neuroscience · 2023Review
- Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network.Frontiers in neuroscience · 2023Article
- Investigation of Deep Spiking Neural Networks Utilizing Gated Schottky Diode as Synaptic Devices.Micromachines · 2022Article
- Bio-Inspired Control System for Fingers Actuated by Multiple SMA Actuators.Biomimetics (Basel, Switzerland) · 2022Article
- A scoping review of artificial intelligence applications in thoracic surgery.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2022Article
- A surrogate gradient spiking baseline for speech command recognition.Frontiers in neuroscience · 2022Article
- Prediction of the Fundus Tessellation Severity With Machine Learning Methods.Frontiers in medicine · 2022Article
- Spike-Representation of EEG Signals for Performance Enhancement of Brain-Computer Interfaces.Frontiers in neuroscience · 2022Article
- Neuronal-Plasticity and Reward-Propagation Improved Recurrent Spiking Neural Networks.Frontiers in neuroscience · 2021Article
- Spatio-Temporal Sequential Memory Model With Mini-Column Neural Network.Frontiers in neuroscience · 2021Article
- Hierarchical Network Connectivity and Partitioning for Reconfigurable Large-Scale Neuromorphic Systems.Frontiers in neuroscience · 2021Article
- Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning.Frontiers in molecular biosciences · 2020Review
- Performance of deep-learning artificial intelligence algorithms in detecting retinopathy of prematurity: A systematic review.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 energy-efficiency 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 only 10 algorithmic time steps and as low as 0.68 times total synaptic operations to classify each audio frame. 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.
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