Investigations on speech emotion recognition using optimized artificial intelligence techniques

Abstract

Speech processing played a vital role in different applications, such newlineas emotion recognition, virtual assistants, voice identification, biometrics newlinesystem, etc. Among that speech emotion recognition helps to identify the newlinepeople mental behaviour and physiological issues. Generally, human newlineemotions are continuously changed that causes the physiological problem and newlinemental disorders. Therefore, new technologies are utilized in this field to newlinerecognize the human emotions. Emotion plays a crucial role in regular human newlineinteractions and facilitates mutual understanding. Therefore, Speech Emotion newlineRecognition (SER) can significantly advantage human-cantered interactive newlinetechnologies since extracted emotion can understand and respond to user newlineneeds. However, predicting the acoustic condition, textual content, and style newlineof emotional expression (e.g., natural or acted) is challenging in SER. newlineMoreover, SER is difficult owing to the affective gap among subjective newlineemotion and the low-level feature. Therefore, the extraction of acoustic newlinefeatures is crucial to speech emotion recognition. newlineAccording to the survey of 2018 and 2020, most of the research newlineapproaches predicts speech emotions from 70 to 80% of accuracy. The speech newlinecharacteristics are semantic independent that requires the optimized newlinetechniques to identify the different emotional states. newline newline