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Resource constrained speech recognition (SPE-RCSR)

Knowledge Distillation for Small-footprint Highway Networks


Deep learning has significantly advanced state-of-the-art of speech
recognition in the past few years. However, compared to conventional
Gaussian mixture acoustic models, neural network models are
usually much larger, and are therefore not very deployable in embedded
devices. Previously, we investigated a compact highway deep
neural network (HDNN) for acoustic modelling, which is a type
of depth-gated feedforward neural network. We have shown that
HDNN-based acoustic models can achieve comparable recognition

Paper Details

Authors:
Liang Lu, Michelle Guo, Steve Renals
Submitted On:
3 March 2017 - 5:15pm
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[1] Liang Lu, Michelle Guo, Steve Renals, "Knowledge Distillation for Small-footprint Highway Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1619. Accessed: Sep. 21, 2017.
@article{1619-17,
url = {http://sigport.org/1619},
author = {Liang Lu; Michelle Guo; Steve Renals },
publisher = {IEEE SigPort},
title = {Knowledge Distillation for Small-footprint Highway Networks},
year = {2017} }
TY - EJOUR
T1 - Knowledge Distillation for Small-footprint Highway Networks
AU - Liang Lu; Michelle Guo; Steve Renals
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1619
ER -
Liang Lu, Michelle Guo, Steve Renals. (2017). Knowledge Distillation for Small-footprint Highway Networks. IEEE SigPort. http://sigport.org/1619
Liang Lu, Michelle Guo, Steve Renals, 2017. Knowledge Distillation for Small-footprint Highway Networks. Available at: http://sigport.org/1619.
Liang Lu, Michelle Guo, Steve Renals. (2017). "Knowledge Distillation for Small-footprint Highway Networks." Web.
1. Liang Lu, Michelle Guo, Steve Renals. Knowledge Distillation for Small-footprint Highway Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1619