Abstract
Speech Emotion Recognition (SER) focuses on understanding the human emotion in a given speech utterance using its acoustic and/or linguistic features. This paper presents a comparison between two speech representation inputs for SER: spectrograms and scalograms. Speech signals from four databases (Emo-DB, RAVDESS, SAVEE, and a mix of all three) were converted into each type of representation and were used to train variations of a convolutional neural network (CNN) VGG16 Model-3. Results show that the scalogram-based models have a higher mean f1-score compared to the spectrogram-based models; however, further analysis indicate that the difference is statistically insignificant at a 95% confidence level. In conclusion, spectrograms and scalograms have statistically the same performance on the systems presented.
Original language | English |
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Title of host publication | 2023 34th Irish Signals and Systems Conference, ISSC 2023 |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 9798350340570 |
DOIs | |
Publication status | Published - 2023 |
Event | 34th Irish Signals and Systems Conference, ISSC 2023 - Dublin, Ireland Duration: 13 Jun 2023 → 14 Jun 2023 |
Publication series
Name | 2023 34th Irish Signals and Systems Conference, ISSC 2023 |
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Conference
Conference | 34th Irish Signals and Systems Conference, ISSC 2023 |
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Country/Territory | Ireland |
City | Dublin |
Period | 13/06/23 → 14/06/23 |
Bibliographical note
Funding Information:This research was conducted with the financial support of Science Foundation Ireland under Grant Agreement No. 13/RC/2106 P2 at the ADAPT SFI Research Centre at University College Dublin. ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology, is funded by Science Foundation Ireland through the SFI Research Centres Programme. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Publisher Copyright:
© 2023 IEEE.