Abstract
In this paper, we present an audio-based emotion recognition model by using OpenSmile, Gaussian mixture models (GMMs) Supervector and Support vector machines (SVM) with Linear kernel. Features are extracted from audio characteristics of emotional video through OpenSmile into Mel-frequency Cepstral Coefficient (MFCC) of 39 dimensions for each video. Furthermore, these features are normalized to the same size using GMM Supervector with 32 mixture components. Finally, data is classified using SVM with Linear Kernel. To evaluate the model, this paper using the AFEW2017 dataset and SAVEE dataset and show comparable the results on the state-of-the-art network. The experimental results perform with 37% on AFEW and 73.5% on SAVEE dataset. Our proposed achieves improved emotion recognition from audio as compared to several other models.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
