| Title |
A Comparison of Effective Feature Vectors for Speech Emotion Recognition |
| Authors |
신보라(Bo-Ra Shin) ; 이석필(Soek-Pil Lee) |
| DOI |
http://doi.org/10.5370/KIEE.2018.67.10.1364 |
| Keywords |
Speech emotion recognition ; Feature extraction ; Speech processing ; Preprocessing |
| Abstract |
Speech emotion recognition, which aims to classify speaker's emotional states through speech signals, is one of the essential tasks for making Human-machine interaction (HMI) more natural and realistic. Voice expressions are one of the main information channels in interpersonal communication. However, existing speech emotion recognition technology has not achieved satisfactory performances, probably because of the lack of effective emotion-related features. This paper provides a survey on various features used for speech emotional recognition and discusses which features or which combinations of the features are valuable and meaningful for the emotional recognition classification. The main aim of this paper is to discuss and compare various approaches used for feature extraction and to propose a basis for extracting useful features in order to improve SER performance. |