ArticleFrontiers in neuroscience2018
A Comparison of Low-Complexity Real-Time Feature Extraction for Neuromorphic Speech Recognition.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model.Frontiers in neuroscience · 2023Article
- Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.Frontiers in neuroscience · 2022Article
- Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review.Frontiers in neural circuits · 2021Review
- How the Brunswikian Lens Model Illustrates the Relationship Between Physiological and Behavioral Signals and Psychological Emotional and Cognitive States.Frontiers in psychology · 2021Article
- Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition.Frontiers in neuroscience · 2020Article
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Authors and funding
6 authors.
Funding
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Abstract
This paper presents a real-time, low-complexity neuromorphic speech recognition system using a spiking silicon cochlea, a feature extraction module and a population encoding method based Neural Engineering Framework (NEF)/Extreme Learning Machine (ELM) classifier IC. Several feature extraction methods with varying memory and computational complexity are presented along with their corresponding classification accuracies. On the N-TIDIGITS18 dataset, we show that a fixed bin size based feature extraction method that votes across both time and spike count features can achieve an accuracy of 95% in software similar to previously report methods that use fixed number of bins per sample while using ~3× less energy and ~25× less memory for feature extraction (~1.5× less overall). Hardware measurements for the same topology show a slightly reduced accuracy of 94% that can be attributed to the extra correlations in hardware random weights. The hardware accuracy can be increased by further increasing the number of hidden nodes in ELM at the cost of memory and energy.
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