The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Small-footprint keyword spotting using deep neural networks
Abstract
Our application requires a keyword spotting system with a small memory footprint, low computational cost, and high precision. To meet these requirements, we propose a simple approach based on deep neural networks. A deep neural network is trained to directly predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score. Keyword recognition results achieve 45% relative improvement with respect to a competitive Hidden Markov Model-based system, while performance in the presence of babble noise shows 39% relative improvement.
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Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
Building high-level features using large scale unsupervised learning
Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695
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Wan, L., Wang, Q., Papir, A., & Moreno, I. L. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8462665 · Cited by 820
Convolutional neural networks for small-footprint keyword spotting
Sainath, T. N., & Parada, C. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-352 · Cited by 521
Self-citationTensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
David, R., Duke, J., Jain, A., Reddi, V. J., Jeffries, N., Li, J., Kreeger, N., Nappier, I., Natraj, M., Regev, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.08678 · Cited by 470
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Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Jiao, L., Qendro, L., & Kawsar, F. (2016) · 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2016.7460664 · Cited by 450
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Metadata
- Authors
- Chen, G., Parada, C., & Heigold, G.
- Venue
- 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2014-05
- Pages
- 4087-4091
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2034940213
- Cited by
- 545
- References
- 2
- FWCI
- 15.42
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, G., Parada, C., & Heigold, G. (2014). Small-footprint keyword spotting using deep neural networks. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2014.6854370
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