Abstract
In this paper, we propose a novel emotion recognition method to reflect affect salient information using acoustic and lexical features. The acoustic features are extracted from the speech signal by applying statistical functionals of emotionally high-level features derived from Deep Neural Network (DNN). These acoustic features are early fused with two types of lexical features extracted from the text transcription of the speech signal, which are the distributed representation and affective lexicon-based dimensions. The fused features are fed to another DNN for utterance-level emotion classification. Experimental results on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) multimodal dataset showed 75.5% in unweighted accuracy recall, which outperformed the best results reported previously in the multimodal emotion recognition using acoustic and lexical features.
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.
