In-depth investigation of speech emotion recognition studies from past to present –The importance of emotion recognition from speech signal for AI–

In the super smart society (Society 5.0), new and rapid methods are needed for speech recognition, emotion recognition, and speech emotion recognition areas to maximize human-machine or human-computer interaction and collaboration. Speech signal contains much information about the speaker, such as age, sex, ethnicity, health condition, emotion, and thoughts. The field of study which analyzes the mood of the person from the speech is called speech emotion recognition (SER). Classifying the emotions from the speech data is a complicated problem for artificial intelligence, and its sub-discipline, machine learning. Because it is hard to analyze the speech signal which contains various frequencies and characteristics. Speech data are digitized with signal processing methods and speech features are obtained. These features vary depending on the emotions such as sadness, fear, anger, happiness, boredom, confusion, etc. Even though different methods have been developed for determining the audio properties and emotion recognition, the success rate varies depending on the languages, cultures, emotions, and data sets. In speech emotion recognition, there is a need for new methods which can be applied in data sets with different sizes, which will increase classification success, in which best properties can be obtained, and which are affordable. The success rates are affected by many factors such as the methods used, lack of speech emotion datasets, the homogeneity of the database, the difficulty of the language (linguistic differences), the noise in audio data and the length of the audio data. Within the scope of this study, studies on emotion recognition from speech signals from past to present have been analyzed in detail. In this study, classification studies based on a discrete emotion model using speech data belonging to the Berlin emotional database (EMO-DB), Italian emotional speech database (EMOVO), The Surrey audio-visual expressed emotion database (SAVEE), Ryerson Audio-Visual Database of Emotional Speech and Song Database (RAVDESS), which are mostly independent of the speaker and content, are examined. The results of both classical classifiers and deep learning methods are compared. Deep learning results are more successful, but classical classification is more important in determining the defining features of speech, song or voice. So It develops feature extraction stage. This study will be able to contribute to the literature and help the researchers in the SER field.