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An application of Artificial Intelligence in emotion detection by speech only

: Emotions of human are very important in our daily communications and recent years have witnessed a lot of research works to develop reliable emotion recognition systems based on various types data sources such as audio and video. Since there is no apparently visual information of human faces, emotion analysis based on only audio data is a very challenging task. In this work, a novel emotion recognition is proposed based on robust features and machine learning from only audio speech. For a person independent emotion recognition system, audio data is used as input to the system from which, Mel Frequency Cepstral Coefficients (MFCC) are calculated as features. The MFCC features are then followed by critical analysis to minimize the inner-class scatterings and maintain max value of the inter-class scatterings. The robust indistinguishable features are then applied with an efficient and fast deep learning approach Neural Structured Learning (NSL) for emotion training and recognition. The proposed approach of combining MFCC, discriminate analysis and NSL generated superior recognition rates compared to other traditional approaches such as MFCC-DBN, MFCC-CNN, and MFCC-RNN during the experiments on an emotion dataset of audio speeches. The system can be adopted in smart environments such as homes or clinics to provide affective healthcare. Since NSL is fast and easy to implement, it can be tried on edge devices with limited datasets collected from edge sensors. Hence, we can push the decision-making step towards where data resides rather than conventionally processing of data and making decisions from faraway of the data sources. The proposed approach can be applied in different practical applications such as understanding peoples’ emotions in their daily life and stress from the voice of the pilots or air traffic controllers in air traffic management systems, and identification of leader’s emotions or intentions behind their speech.