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Resource constrained speech recognition (SPE-RCSR)

RECOGNIZING ZERO-RESOURCED LANGUAGES BASED ON MISMATCHED MACHINE TRANSCRIPTIONS


Mismatched crowdsourcing based probabilistic human transcription has been proposed recently for training and adapting acoustic models for zero-resourced languages where we do not have any native transcriptions. This paper describes a machine transcription based phone recognition system for recognizing zero-resourced languages and compares it with baseline systems of MAP adaptation and semi-supervised self training.

Poster.pdf

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Paper Details

Authors:
Mark Hasegawa-Johnson, Nancy F. Chen
Submitted On:
12 April 2018 - 7:52pm
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Poster.pdf

(798 downloads)

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[1] Mark Hasegawa-Johnson, Nancy F. Chen, "RECOGNIZING ZERO-RESOURCED LANGUAGES BASED ON MISMATCHED MACHINE TRANSCRIPTIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2520. Accessed: Dec. 11, 2018.
@article{2520-18,
url = {http://sigport.org/2520},
author = {Mark Hasegawa-Johnson; Nancy F. Chen },
publisher = {IEEE SigPort},
title = {RECOGNIZING ZERO-RESOURCED LANGUAGES BASED ON MISMATCHED MACHINE TRANSCRIPTIONS},
year = {2018} }
TY - EJOUR
T1 - RECOGNIZING ZERO-RESOURCED LANGUAGES BASED ON MISMATCHED MACHINE TRANSCRIPTIONS
AU - Mark Hasegawa-Johnson; Nancy F. Chen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2520
ER -
Mark Hasegawa-Johnson, Nancy F. Chen. (2018). RECOGNIZING ZERO-RESOURCED LANGUAGES BASED ON MISMATCHED MACHINE TRANSCRIPTIONS. IEEE SigPort. http://sigport.org/2520
Mark Hasegawa-Johnson, Nancy F. Chen, 2018. RECOGNIZING ZERO-RESOURCED LANGUAGES BASED ON MISMATCHED MACHINE TRANSCRIPTIONS. Available at: http://sigport.org/2520.
Mark Hasegawa-Johnson, Nancy F. Chen. (2018). "RECOGNIZING ZERO-RESOURCED LANGUAGES BASED ON MISMATCHED MACHINE TRANSCRIPTIONS." Web.
1. Mark Hasegawa-Johnson, Nancy F. Chen. RECOGNIZING ZERO-RESOURCED LANGUAGES BASED ON MISMATCHED MACHINE TRANSCRIPTIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2520

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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Slides for ICASSP 2017

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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: Dec. 11, 2018.
@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