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Title: Ensemble learning of hybrid acoustic features for speech emotion recognition
Authors: Zvarevashe, Kudakwashe
Olugbara, Oludayo
Keywords: emotion recognition
ensemble algorithm
feature extraction
machine learning
supervised learning
Issue Date: Mar-2020
Publisher: MDPI
Citation: Zvarevashe, K. and Olugbara, O. (2020). Ensemble learning of hybrid acoustic features for speech emotion recognition. Algorithms, 13 (70). http://doi:10.3390/a13030070
Abstract: Automatic recognition of emotion is important for facilitating seamless interactivity between a human being and intelligent robot towards the full realization of a smart society. The methods of signal processing and machine learning are widely applied to recognize human emotions based on features extracted from facial images, video files or speech signals. However, these features were not able to recognize the fear emotion with the same level of precision as other emotions. The authors propose the agglutination of prosodic and spectral features from a group of carefully selected features to realize hybrid acoustic features for improving the task of emotion recognition. Experiments were performed to test the effectiveness of the proposed features extracted from speech files of two public databases and used to train five popular ensemble learning algorithms. Results show that random decision forest ensemble learning of the proposed hybrid acoustic features is highly effective for speech emotion recognition.
ISSN: 1999-4893
Appears in Collections:Department of Analytics and Informatics Staff Publications

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