About: Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #67452 Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition
sameAs	Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition
http://eprints.org/ontology/hasAccepted	Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition (PDF)
http://eprints.org/ontology/hasDocument	Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition (PDF) Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition (Other) Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition (Other) Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition (Other) Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition (Other) Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition (Other)
dc:hasVersion	Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition (PDF)
Title	Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2018-09-06
Creator	Li-Rong Dai Jian Tang Yan Song I. McLoughlin
status	peer reviewed published
abstract	Motivated by recent advances in computer vision research, this paper proposes a novel acoustic model called Densely Connected Residual Network (DenseRNet) for multichannel speech recognition. This combines the strength of both DenseNet and ResNet. It adopts the basic "building blocks" of ResNet with different convolutional layers, receptive field sizes and growth rates as basic components that are densely connected to form so-called denseR blocks. By concatenating the feature maps of all preceding layers as inputs, DenseRNet can not only strengthen gradient back-propagation for the vanishing-gradient problem, but also exploit multi-resolution feature maps. Preliminary experimental results on CHiME-3 have shown that DenseRNet achieves a word error rate (WER) of 7.58% on beamforming-enhanced speech with six channel real test data by cross entropy criteria training while WER is 10.23% for the official baseline. Besides, additional experimental results are also presented to demonstrate that DenseRNet exhibits the robustness to beamforming-enhanced speech as well as near and far-field speech.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/67452#authors
presented at	Interspeech 2018
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #67452 Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition

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