Deep Learning for the Recognition of Human Speech

Abstract

Abstract—Humans primarily communicate through speech, and language is their main means of doing so. Emotion is crucial to social connection. Knowing how to recognise the emotions in a speech is important and challenging given that we are dealing with human-machine interaction. Depending on the mood a person is going through, they will express themselves differently. When someone expresses their emotions, each one has a distinctive energy, pitch, and tone fluctuation that is categorised according to the situation. The identification of spoken emotions is thus a future goal for computer vision. The objective of our project is to develop intelligent speechthat utilises a convolutional neural network to recognise emotions. which uses a variety of modules for emotion recognition and classifier. Keywords— Human speech, Emotion, recognition of speech, GMM, Mel-Frequency-Cepstrum Coefficient (MFCC), linear prediction Cepstral coefficients (LPCC) , (LSTM)