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A Study of All-Convolutional Encoders for Connectionist Temporal Classification (Poster)

Abstract: 

Connectionist temporal classification (CTC) is a popular sequence prediction approach for automatic speech recognition that is typically used with models based on recurrent neural networks (RNNs). We explore whether deep convolutional neural networks (CNNs) can be used effectively instead of RNNs as the "encoder" in CTC. CNNs lack an explicit representation of the entire sequence, but have the advantage that they are much faster to train. We present an exploration of CNNs as encoders for CTC models, in the context of character-based (lexicon-free) automatic speech recognition. In particular, we explore a range of one-dimensional convolutional layers, which are particularly efficient. We compare the performance of our CNN-based models against typical RNNbased models in terms of training time, decoding time, model size and word error rate (WER) on the Switchboard Eval2000 corpus. We find that our CNN-based models are close in performance to LSTMs, while not matching them, and are much faster to train and decode.

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Paper Details

Authors:
Kalpesh Krishna, Liang Lu, Kevin Gimpel, Karen Livescu
Submitted On:
14 April 2018 - 6:13am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Kalpesh Krishna
Paper Code:
2192
Document Year:
2018
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study-convolutional-encoders.pdf

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[1] Kalpesh Krishna, Liang Lu, Kevin Gimpel, Karen Livescu, "A Study of All-Convolutional Encoders for Connectionist Temporal Classification (Poster)", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2816. Accessed: Aug. 10, 2020.
@article{2816-18,
url = {http://sigport.org/2816},
author = {Kalpesh Krishna; Liang Lu; Kevin Gimpel; Karen Livescu },
publisher = {IEEE SigPort},
title = {A Study of All-Convolutional Encoders for Connectionist Temporal Classification (Poster)},
year = {2018} }
TY - EJOUR
T1 - A Study of All-Convolutional Encoders for Connectionist Temporal Classification (Poster)
AU - Kalpesh Krishna; Liang Lu; Kevin Gimpel; Karen Livescu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2816
ER -
Kalpesh Krishna, Liang Lu, Kevin Gimpel, Karen Livescu. (2018). A Study of All-Convolutional Encoders for Connectionist Temporal Classification (Poster). IEEE SigPort. http://sigport.org/2816
Kalpesh Krishna, Liang Lu, Kevin Gimpel, Karen Livescu, 2018. A Study of All-Convolutional Encoders for Connectionist Temporal Classification (Poster). Available at: http://sigport.org/2816.
Kalpesh Krishna, Liang Lu, Kevin Gimpel, Karen Livescu. (2018). "A Study of All-Convolutional Encoders for Connectionist Temporal Classification (Poster)." Web.
1. Kalpesh Krishna, Liang Lu, Kevin Gimpel, Karen Livescu. A Study of All-Convolutional Encoders for Connectionist Temporal Classification (Poster) [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2816