End-to-End Spiking Neural Network for Speech Recognition Using Resonating Input Neurons

Author:

Auge DanielORCID,Hille JulianORCID,Kreutz FelixORCID,Mueller EtienneORCID,Knoll AloisORCID

Publisher

Springer International Publishing

Reference26 articles.

1. Abdel-Hamid, O., Mohamed, A.R., Jiang, H., Penn, G.: Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4277–4280. IEEE (2012)

2. Auge, D., Mueller, E.: Resonate-and-fire neurons as frequency selective input encoders for spiking neural networks. TUM (Technical Report) (2020)

3. Banbury, C., MicroNets: neural network architectures for deploying TinyML applications on commodity microcontrollers. arXiv preprint arXiv:2010.11267 (2020)

4. Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., Maass, W.: Long short-term memory and learning-to-learn in networks of spiking neurons. In: Advances in Neural Information Processing Systems, pp. 787–797 (2018)

5. Blouw, P., Choo, X., Hunsberger, E., Eliasmith, C.: Benchmarking keyword spotting efficiency on neuromorphic hardware. In: Proceedings of the 7th Annual Neuro-Inspired Computational Elements Workshop, pp. 1–8 (2019)

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