Hybrid deep learning models based emotion recognition with speech signals

Emotion recognition is one of the most important components of human-computer interaction, and it is something that can be performed with the use of voice signals. It is not possible to optimise the process of feature extraction as well as the classification process at the same time while utilising conventional approaches. Research is increasingly focusing on many different types of "deep learning" in an effort to discover a solution to these difficulties. In today's modern world, the practise of applying deep learning algorithms to categorization problems is becoming increasingly important. However, the advantages available in one model is not available in another model. This limits the practical feasibility of such approaches. The main objective of this work is to explore the possibility of hybrid deep learning models for speech signal-based emotion identification. Two methods are explored in this work: CNN and CNN-LSTM. The first model is the conventional one and the second is the hybrid model. TESS database is used for the experiments and the results are analysed in terms of various accuracy measures. An average accuracy of 97% for CNN and 98% for CNN-LSTM is achieved with these models.