Feature Selection Enhancement and Feature Space Visualization for Speech-Based Emotion Recognition

• URL: http://arxiv.org/abs/2208.09269v1
• Date: Fri, 19 Aug 2022 11:29:03 GMT
• Title: Feature Selection Enhancement and Feature Space Visualization for Speech-Based Emotion Recognition
• Authors: Sofia Kanwal, Sohail Asghar, Hazrat Ali
• Abstract summary: We present speech features enhancement strategy that improves speech emotion recognition. The strategy is compared with the state-of-the-art methods used in the literature. 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.
• Score: 2.223733768286313
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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: 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.

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