Feature selection enhancement and feature space visualization for speech-based emotion recognition
<p dir="ltr">Robust speech emotion recognition relies on the quality of the speech features. We present speech features enhancement strategy that improves speech emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We identified subsets from the features set and a...
محفوظ في:
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| مؤلفون آخرون: | , |
| منشور في: |
2022
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513516849856512 |
|---|---|
| author | Sofia Kanwal (12256565) |
| author2 | Sohail Asghar (11017743) Hazrat Ali (421019) |
| author2_role | author author |
| author_facet | Sofia Kanwal (12256565) Sohail Asghar (11017743) Hazrat Ali (421019) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sofia Kanwal (12256565) Sohail Asghar (11017743) Hazrat Ali (421019) |
| dc.date.none.fl_str_mv | 2022-11-04T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.7717/peerj-cs.1091 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Feature_selection_enhancement_and_feature_space_visualization_for_speech-based_emotion_recognition/25539598 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Data management and data science Human-centred computing Machine learning Feature selection Feature space visualization t-SNE graphs SVM Speech emotion recognition Machine learning Speaker-independent emotion recognition |
| dc.title.none.fl_str_mv | Feature selection enhancement and feature space visualization for speech-based emotion recognition |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Robust speech emotion recognition relies on the quality of the speech features. We present speech features enhancement strategy that improves speech emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We identified subsets from the features set and applied principle component analysis to the subsets. Finally, the features are fused horizontally. The resulting feature set is analyzed using t-distributed neighbour embeddings (t-SNE) before the application of features for emotion recognition. The method is compared with the state-of-the-art methods used in the literature. The empirical evidence is drawn using two well-known datasets: Berlin Emotional Speech Dataset (EMO-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for two languages, German and English, respectively. Our method achieved an average recognition gain of 11.5% for six out of seven emotions for the EMO-DB dataset, and 13.8% for seven out of eight emotions for the RAVDESS dataset as compared to the baseline study.</p><h2>Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.1091" target="_blank">https://dx.doi.org/10.7717/peerj-cs.1091</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e95052248950c6856fa1caec445da0c1 |
| identifier_str_mv | 10.7717/peerj-cs.1091 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25539598 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Feature selection enhancement and feature space visualization for speech-based emotion recognitionSofia Kanwal (12256565)Sohail Asghar (11017743)Hazrat Ali (421019)Information and computing sciencesData management and data scienceHuman-centred computingMachine learningFeature selectionFeature space visualizationt-SNE graphsSVMSpeech emotion recognitionMachine learningSpeaker-independent emotion recognition<p dir="ltr">Robust speech emotion recognition relies on the quality of the speech features. We present speech features enhancement strategy that improves speech emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We identified subsets from the features set and applied principle component analysis to the subsets. Finally, the features are fused horizontally. The resulting feature set is analyzed using t-distributed neighbour embeddings (t-SNE) before the application of features for emotion recognition. The method is compared with the state-of-the-art methods used in the literature. The empirical evidence is drawn using two well-known datasets: Berlin Emotional Speech Dataset (EMO-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for two languages, German and English, respectively. Our method achieved an average recognition gain of 11.5% for six out of seven emotions for the EMO-DB dataset, and 13.8% for seven out of eight emotions for the RAVDESS dataset as compared to the baseline study.</p><h2>Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.1091" target="_blank">https://dx.doi.org/10.7717/peerj-cs.1091</a></p>2022-11-04T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.7717/peerj-cs.1091https://figshare.com/articles/journal_contribution/Feature_selection_enhancement_and_feature_space_visualization_for_speech-based_emotion_recognition/25539598CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/255395982022-11-04T03:00:00Z |
| spellingShingle | Feature selection enhancement and feature space visualization for speech-based emotion recognition Sofia Kanwal (12256565) Information and computing sciences Data management and data science Human-centred computing Machine learning Feature selection Feature space visualization t-SNE graphs SVM Speech emotion recognition Machine learning Speaker-independent emotion recognition |
| status_str | publishedVersion |
| title | Feature selection enhancement and feature space visualization for speech-based emotion recognition |
| title_full | Feature selection enhancement and feature space visualization for speech-based emotion recognition |
| title_fullStr | Feature selection enhancement and feature space visualization for speech-based emotion recognition |
| title_full_unstemmed | Feature selection enhancement and feature space visualization for speech-based emotion recognition |
| title_short | Feature selection enhancement and feature space visualization for speech-based emotion recognition |
| title_sort | Feature selection enhancement and feature space visualization for speech-based emotion recognition |
| topic | Information and computing sciences Data management and data science Human-centred computing Machine learning Feature selection Feature space visualization t-SNE graphs SVM Speech emotion recognition Machine learning Speaker-independent emotion recognition |