Faculty of Information Technology, BUT

Publication Details

Residual Memory Networks: Feed-forward approach to learn long-term temporal dependencies

BASKAR Murali K., KARAFIÁT Martin, BURGET Lukáš, VESELÝ Karel, GRÉZL František and ČERNOCKÝ Jan. Residual Memory Networks: Feed-forward approach to learn long-term temporal dependencies. In: Proceedings of ICASSP 2017. New Orleans: IEEE Signal Processing Society, 2017, pp. 4810-4814. ISBN 978-1-5090-4117-6.
Czech title
Residuální paměťové sítě: nerekurentní přístup k učení dlouhých časových závislostí
Type
conference paper
Language
english
Authors
URL
Keywords
Automatic speech recognition, LSTM, RNN, Residual memory networks.
Abstract
This paper is about residual memory networks (RMN) structure, a variant of feed-forward network to capture temporal context.
Annotation
Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep RNN. In case of feed-forward networks training deep structures is simple and faster while learning long-term temporal information is not possible. In this paper we propose a residual memory neural network (RMN) architecture to model short-time dependencies using deep feed-forward layers having residual and time delayed connections. The residual connection paves way to construct deeper networks by enabling unhindered flow of gradients and the time delay units capture temporal information with shared weights. The number of layers in RMN signifies both the hierarchical processing depth and temporal depth. The computational complexity in training RMN is significantly less when compared to deep recurrent networks. RMN is further extended as bi-directional RMN (BRMN) to capture both past and future information. Experimental analysis is done on AMI corpus to substantiate the capability of RMN in learning long-term information and hierarchical information. Recognition performance of RMN trained with 300 hours of Switchboard corpus is compared with various state-of-the-art LVCSR systems. The results indicate that RMN and BRMN gains 6 % and 3.8 % relative improvement over LSTM and BLSTM networks.
Published
2017
Pages
4810-4814
Proceedings
Proceedings of ICASSP 2017
Conference
42nd IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, USA, US
ISBN
978-1-5090-4117-6
Publisher
IEEE Signal Processing Society
Place
New Orleans, US
DOI
BibTeX
@INPROCEEDINGS{FITPUB11467,
   author = "K. Murali Baskar and Martin Karafi\'{a}t and Luk\'{a}\v{s} Burget and Karel Vesel\'{y} and Franti\v{s}ek Gr\'{e}zl and Jan \v{C}ernock\'{y}",
   title = "Residual Memory Networks: Feed-forward approach to learn long-term temporal dependencies",
   pages = "4810--4814",
   booktitle = "Proceedings of ICASSP 2017",
   year = 2017,
   location = "New Orleans, US",
   publisher = "IEEE Signal Processing Society",
   ISBN = "978-1-5090-4117-6",
   doi = "10.1109/ICASSP.2017.7953070",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11467"
}
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