Search
Abstract
In this research, a study of cross-linguistic speech emotion recognition is performed. For this purpose, emotional data of different languages (English, Lithuanian, German, Spanish, Serbian, and Polish) are collected, resulting in a cross-linguistic speech emotion dataset with the size of more than 10.000 emotional utterances. Despite the bi-modal character of the databases gathered, our focus is on the acoustic representation only. The assumption is that the speech audio signal carries sufficient emotional information to detect and retrieve it. Several two-dimensional acoustic feature spaces, such as cochleagrams, spectrograms, mel-cepstrograms, and fractal dimension-based space, are employed as the representations of speech emotional features. A convolutional neural network (CNN) is used as a classifier. The results show the superiority of cochleagrams over other feature spaces utilized. In the CNN-based speaker-independent cross-linguistic speech emotion recognition (SER) experiment, the accuracy of over 90% is achieved, which is close to the monolingual case of SER.
Citations
-
2 4
CrossRef
-
0
Web of Science
-
2 9
Scopus
Authors (6)
-
Gintautas Tamulevicius dr
-
Grazina Korvel
-
Anil Bora Yayak
-
Povilas Treigys dr
-
Jolita Bernataviciene
Cite as
Full text
- Publication version
- Accepted or Published Version
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Electronics
no. 9,
ISSN: 2079-9292 - Language:
- English
- Publication year:
- 2020
- Bibliographic description:
- Tamulevicius G., Korvel G., Yayak A., Treigys P., Bernataviciene J., Kostek B.: A Study of Cross-Linguistic Speech Emotion Recognition Based on 2D Feature Spaces// Electronics -Vol. 9,iss. 10 (2020), s.1725-
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/electronics9101725
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
seen 173 times
Recommended for you
Introduction to the special issue on machine learning in acoustics
- Z. Michalopoulou,
- P. Gerstoft,
- B. Kostek
- + 1 authors
Investigating Feature Spaces for Isolated Word Recognition
- G. Korvel,
- G. Tamulevicus,
- P. Treigys
- + 2 authors
Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
- G. Korvel,
- P. Treigys,
- G. Tamulevicus
- + 2 authors
Investigating Feature Spaces for Isolated Word Recognition
- P. Treigys,
- G. Korvel,
- G. Tamulevicius
- + 2 authors