Abstract
Speech Emotion Recognition (SER) systems use machine learning to detect emotions from audio speech irrespective of the semantic context of the audio. Current research has limitations due to the complexity posed by language, accent, gender, age and intensity present in the speech and developing accurate SER systems remain an open challenge. This study focuses on a novel approach for developing a deep learning system which unifies four datasets, i.e., RAVDESS, TESS, CREMA-D and SAVEE to detect emotions from speech. This combination of datasets is used along with the most relevant features, i.e., Zero Crossing Rate (ZCR), Chroma Feature, MFCC, Root Mean Square (RMS) and Mel Spectrum. A 4-layer Convolutional Neural Network (CNN) is used on the training data achieving an accuracy of 76%. The results show that the proposed approach increases the reliability and makes the model less variant to new data compared to models trained on single datasets. The shortcomings of the current approach and their respective solutions are also discussed.
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References
Abbaschian, B.J., Sierra-Sosa, D., Elmaghraby, A.: Deep learning techniques for speech emotion recognition, from databases to models. Sensors 21(4), 1249 (2021). https://doi.org/10.3390/s21041249
Bhavan, A., Chauhan, P., Hitkul, Shah, R.R.: Bagged support vector machines for emotion recognition from speech. Knowl.-Based Syst. 184, 104886 (2019). https://doi.org/10.1016/j.knosys.2019.104886
Fayek, H.M., Lech, M., Cavedon, L.: Evaluating deep learning architectures for speech emotion recognition. Neural Netw. 92, 60–68 (2017). https://doi.org/10.1016/j.neunet.2017.02.013
Uddin, M., Nilsson, E.G.: Emotion recognition using speech and neural structured learning to facilitate edge intelligence. Eng. Appl. Artif. Intell. 94, 103775 (2020). https://doi.org/10.1016/j.engappai.2020.103775
Bagherzadeh, S., Maghooli, K., Shalbaf, A., Maghsoudi, A.: Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals. Biomed. Signal Process. Control 75, 103544 (2022). https://doi.org/10.1016/j.bspc.2022.103544
Livingstone, S.R., Russo, F.A.: The Ryerson audio-visual database of emotional speech and song (RAVDESS): a dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5), e0196391 (2018). https://doi.org/10.1371/journal.pone.0196391
Pichora-Fuller, M.K., Dupuis, K.: Toronto emotional speech set (TESS). Borealis (2020). https://doi.org/10.5683/SP2/E8H2MF
Cao, H., Cooper, D.G., Keutmann, M.K., Gur, R.C., Nenkova, A., Verma, R.: CREMA-D: crowd-sourced emotional multimodal actors dataset. IEEE Trans. Affect. Comput. 5(4), 377–390 (2014). https://doi.org/10.1109/TAFFC.2014.2336244
Jackson, P., Ul Haq, S.: Surrey audio-visual expressed emotion (SAVEE) database (2011)
Panagiotakis, C., Tziritas, G.: A speech/music discriminator based on RMS and zero-crossings. IEEE Trans. Multimed. 7(1), 155–166 (2005). https://doi.org/10.1109/TMM.2004.840604
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Ahmed, W., Riaz, S., Iftikhar, K., Konur, S. (2023). Speech Emotion Recognition Using Deep Learning. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XL. SGAI 2023. Lecture Notes in Computer Science(), vol 14381. Springer, Cham. https://doi.org/10.1007/978-3-031-47994-6_14
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DOI: https://doi.org/10.1007/978-3-031-47994-6_14
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