On Using Stateful LSTM Networks for Key-Phrase Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43956398" target="_blank" >RIV/49777513:23520/19:43956398 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-27947-9_22" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-27947-9_22</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-27947-9_24" target="_blank" >10.1007/978-3-030-27947-9_24</a>
Alternative languages
Result language
angličtina
Original language name
On Using Stateful LSTM Networks for Key-Phrase Detection
Original language description
In this paper, we focus on LSTM (Long Short-Term Memory) networks and their implementation in a popular framework called Keras. The goal is to show how to take advantage of their ability to pass the context by holding the state and to clear up what the stateful property of LSTM Recurrent Neural Network implemented in Keras actually means. The main outcome of the work is then a general algorithm for packing arbitrary context-dependent data, capable of 1/ packing the data to fit the stateful models; 2/ making the training process efficient by supplying multiple frames together; 3/ on-the-fly (frame-by-frame) prediction by the trained model. Two training methods are presented, a window-based approach is compared with a fully-stateful approach. The analysis is performed on the Speech commands dataset. Finally, we give guidance on how to use stateful LSTMs to create a key-phrase detection system.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Text, Speech, and Dialogue 22nd International Conference, TSD 2019, Ljubljana,Slovenia, September 11-13, 2019, Proceedings
ISBN
978-3-030-27946-2
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
12
Pages from-to
287-298
Publisher name
Springer
Place of publication
Cham
Event location
Ljubljana, Slovenia
Event date
Sep 11, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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