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

Autor:	Kanwal, Sofia, Asghar, Sohail, Ali, Hazrat
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at PeerJ Computer Science
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.09269 Zobrazit plný text záznamu View this record from Arxiv