ZČU
Publikace
Detail publikace
Citace
: On Using Stateful LSTM Networks for Key-Phrase Detection . Text, Speech and Dialogue, 24, Springer Nature, Kamil Ekštein, 2019.
Abstrakt
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.
Detail publikace
| Název: | On Using Stateful LSTM Networks for Key-Phrase Detection |
|---|---|
| Autor: | M. Bulín ; L. Šmídl ; J. Švec |
| Jazyk publikace: | anglicky |
| Datum vydání: | 11.9.2019 |
| Rok vydání: | 2019 |
| Typ publikace: | Stať ve sborníku |
| Název časopisu / knihy: | Text, Speech and Dialogue |
| Název kapitoly: | 24 |
| Editor: | Kamil Ekštein |
| Nakladatel: | Springer Nature |
| Datum: | 11.9.2019 - 13.9.2019 |
Klíčová slova
LSTM, Stateful, Context modeling, Key-phrase detection, ASR
BibTeX
@ARTICLE{MBulin_2019_OnUsingStateful,
author = {M. Bul\'{i}n and L. \v{S}m\'{i}dl and J. \v{S}vec},
title = {On Using Stateful LSTM Networks for Key-Phrase Detection},
year = {2019},
publisher = {Springer Nature},
journal = {Text, Speech and Dialogue},
editor = {Kamil Ek\v{s}tein},
chapter = {24},
url = {http://www.kky.zcu.cz/en/publications/MBulin_2019_OnUsingStateful},
}