LSTM Network based on Prosodic Features for the Classification of Injunction in French Oral Utterances

Asma Bougrine, Philippe Ravier, Abdenour Hacine-Gharbi, Hanane Ouachour

2022

Abstract

The classification of injunction in french oral speech is a difficult task since no standard linguistic structure is known in the french language. Thus, prosodic features of the speech could be permitted indicators for this task, especially the logarithmic energy. Our aim is to validate the predominance of the log energy prosodic feature by using conventional classifiers such as SVM or K-NN. Second, we intend to improve the classification rates by using a deep LSTM recurrent network. When applied on the RAVIOLI database, the log energy feature showed indeed the best classification rates (CR) for all classifiers with CR = 82% for SVM and CR = 71.42% for K-NN. When applying the LSTM network on our data, the CR reached a not better value of 79.49% by using the log energy feature alone. More surprisingly, the CR significantly increased to 96.15% by using the 6 prosodic features. We conclude that deep learning methods need as much data as possible for reaching high performance, even the less informative ones, especially when the dataset is small. The counterpart of deep learning methods remains the difficulty of optimal parameters tuning.

Paper Citation

in Harvard Style

Bougrine A., Ravier P., Hacine-Gharbi A. and Ouachour H. (2022). LSTM Network based on Prosodic Features for the Classification of Injunction in French Oral Utterances. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 730-736. DOI: 10.5220/0010910500003122

in Bibtex Style

@conference{icpram22,
author={Asma Bougrine and Philippe Ravier and Abdenour Hacine-Gharbi and Hanane Ouachour},
title={LSTM Network based on Prosodic Features for the Classification of Injunction in French Oral Utterances},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={730-736},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010910500003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - LSTM Network based on Prosodic Features for the Classification of Injunction in French Oral Utterances
SN - 978-989-758-549-4
AU - Bougrine A.
AU - Ravier P.
AU - Hacine-Gharbi A.
AU - Ouachour H.
PY - 2022
SP - 730
EP - 736
DO - 10.5220/0010910500003122