Multi-lingual Speech Emotion Recognition System Using Machine Learning
| dc.authorid | https://orcid.org/0000-0003-1755-3130 | |
| dc.authorid | https://orcid.org/0000-0001-8440-5539 | |
| dc.authorid | https://orcid.org/0000-0002-3028-0416 | |
| dc.contributor.author | Çolakoğlu, Emel | |
| dc.contributor.author | Hızlısoy, Serhat | |
| dc.contributor.author | Arslan, Recep Sinan | |
| dc.date.accessioned | 2024-10-24T11:56:39Z | |
| dc.date.available | 2024-10-24T11:56:39Z | |
| dc.date.issued | 2024 | |
| dc.department | Başka Kurum | |
| dc.description.abstract | Predicting emotions from speech in different languages with high accuracy has been a challengingtask for researchers in recent years. When we delve into the studies conducted in this field, it isclear that researchers generally try to recognize emotions from speech in their traditional language.However, these studies cannot be generalized for multi-lingual environments around the globe.The Turkish speech emotional dataset, which was created for use in our previous studies, wasfurther expanded for use in this study too. Emo-db dataset was also used to benchmark the successof the proposed model. Various pre-processing stages such as standardization, sorting andresampling were applied to the data in the datasets to increase the performance of the model.OpenSMILE toolbox, which is frequently encountered in studies, was used to obtain features thatprovide meaningful information corresponding to the emotion in speech, and thousands of featureswere obtained from emobase2010 and emo_large feature sets. 8 different machine learningalgorithms were used in the model to classify 4 different emotions for the Turkish dataset and 7different emotions for the Emo-db dataset. The best recognition rates were achieved with 92.73%and 96.3%, respectively, for the Turkish dataset consisting of 1099 records and the Emo-db datasetconsisting of 535 records, using the Emobase2010 as a feature set and Logistic Regression as aclassifier. | |
| dc.identifier.citation | Çolakoğlu, E., Hızlısoy, S., Arslan, R. S., (2024) Multi-lingual Speech Emotion Recognition System Using Machine Learning. Selcuk University Journal of Engineering Sciences, 23(1), 1-11. | |
| dc.identifier.endpage | 11 | |
| dc.identifier.issn | 2757-8828 | |
| dc.identifier.issue | 1 | |
| dc.identifier.startpage | 1 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/53081 | |
| dc.identifier.volume | 23 | |
| dc.language.iso | en | |
| dc.publisher | Selçuk Üniversitesi | |
| dc.relation.ispartof | Selcuk University Journal of Engineering Sciences | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Başka Kurum Yazarı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.title | Multi-lingual Speech Emotion Recognition System Using Machine Learning | |
| dc.type | Article |
