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A Separable Temporal Convolution Neural Network with Attention for Small-Footprint Keyword Spotting
- Publication Year :
- 2021
-
Abstract
- Keyword spotting (KWS) on mobile devices generally requires a small memory footprint. However, most current models still maintain a large number of parameters in order to ensure good performance. To solve this problem, this paper proposes a separable temporal convolution neural network with attention, it has a small number of parameters. Through the time convolution combined with attention mechanism, a small number of parameters model (32.2K) is implemented while maintaining high performance. The proposed model achieves 95.7% accuracy on the Google Speech Commands dataset, which is close to the performance of Res15(239K), the state-of-the-art model in KWS at present.<br />Comment: arXiv admin note: text overlap with arXiv:2108.12146
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2109.00260
- Document Type :
- Working Paper