Attention-based Speech Emotion Recognition Approach for Medical Application

Abstract

Abstract: Presently AI is used in various medical fields. Mental health is an important part of the overall health of a person and speech is the primary form for expression of emotion. Thus, Speech Emotion Recognition can be used to understand emotions of the person and help doctors focus on the cure. Speech Emotion recognition is analysis and classification of speech signals to detect the underlying emotions. This paper proposes a model for speech emotion recognition using an attention mechanism. The model is developed using Mel-frequency cepstral coefficients (MFCCs), and a combination of 2D CNN layers and LSTM recurrent layers for temporal aggregation. The proposed model is evaluated using a dataset of speech recordings containing eight emotion categories. The results show that the model achieves 89% accuracy. The attention mechanism is found to improve the recognition performance by focusing on relevant emotional information and ignoring irrelevant information. This research has potential applications in clinical settings in detection as well as treatment for mental health issues.