Deep Analysis for Speech Emotion Recognization

Emotions play a vital part in human interactions and are important determinants of user happiness and consumer sentiment. The creation of human-computer interaction (HCI) systems depends on speech emotion recognition (SER) modules. Systems for analysing and classifying speech signals in order to identify the emotions they contain are known as speech emotion recognition (SER) systems. SER is a well-established and fast developing branch of signal processing. More data will be available to learn from as speech emotion detection systems become more popular, helping them to improve their performance. This study also includes a discussion of certain common machine learning approaches, such as ensemble methods, deep learning methods, and classifier enhancement methods. Attention-based deep neural networks (DNNs) are also highlighted in this paper, which is getting a lot of attention in this domain. The findings of this study show that this field of study is being investigated steadily. The scientific community in this area could benefit from this analysis’s inspiration and motivation.