A Study of Sample Size Requirement and Effectiveness of Mel-Scaled Features for Small-Footprint Keyword Spotting in a Limited Dataset Environment
Author:
Affiliation:
1. San Diego State University,Dept. of Electrical and Computer Engi.,San Diego,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10192575/10192940/10193301.pdf?arnumber=10193301
Reference17 articles.
1. LSTMs for Keyword Spotting with ReRAM-Based Compute-In-Memory Architectures
2. Max-Pooling Loss Trained Long Short Term Memory Network For Small-Footprint Keyword Spotting;sun;Proc IEEE/ACL Workshop Spoken Lang Technol (SLT),2016
3. Deep Spoken Keyword Spotting: An Overview
4. Reduced Model Size Deep Convolutional Neural Networks for Small-Footprint Keyword Spotting
5. Deep Convolutional Spiking Neural Networks for Keyword Spotting
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