A CONTENT ANALYSIS OF THE RESEARCH APPROACHES IN SPEECH EMOTION RECOGNITION

Numerous researchers have conducted studies on the recognition of emotion from human speech with different study designs. Speech Emotion Recognition (SER) is a specific class of signal processing where the main goal is to identify the emotional state of people from voice. SER processes are extensively initialized with the extraction of acoustic features from speech signal via signal processing. Subsequent to selection of the most relevant speech features, a model explaining the relations between the emotions and the voice is searched. Effects of acoustic parameters, the validity of the data used, and performance of the classifiers have been the vital issues for emotion recognition research field. In this study, a content analysis of the studies on the SER based on acoustic parameters was performed. 81 articles (published in the indexed journals) have been assessed by the approaches used for emotion labelling, acoustic features and classifiers and the database used. In addition to that analysis, effect of the acoustic parameters on the status of emotion is also extracted as a summary. The main aim of this study is to: describe the features of the databases in use and to create a brief on the efficiency of acoustic parameters and the classifiers employed by the previous studies. Thereby, it is expected to shed light on the study design for the future studies.