Sequential Pattern Analysis of Emotion in Speech With LSTM

Automatic speech emotion recognition poses great challenges due to the large differences between acoustic properties and human perception. its performance relies on a variety of acoustic discriminations suited to specific tasks. it makes things difficult when people share different opinions. speech emotions have different strengths with different sounds at different points in the middle phase. therefore, speech recognition has become a difficult task in computer processing. this article introduces deep learning techniques and discusses some recent literature applying these techniques to speech recognition. in this context, the recurrent neural network (rnn) algorithm has become the basis of speech recognition. the classifers used to detect emotions like happiness, surprise, anger, neutral state, sadness, etc. the foundation of the speech emotion recognition system lies in a database comprising emotional speech samples. extracted from these samples are key features such as energy, pitch, and mel frequency cepstrum coefficient (mfcc). the evaluation of classification performance is contingent on the analysis of these extracted features. it includes different models for emotion recognition that use separate components to distinguish emotions. materials needed to train emotional speech may include speech patterns. the librosa package helps remove obtrusive features from the speech model. the key to classification is based on the quality of extracted features. finally, through this process we can identify and analyze the emotional content embedded in a speech signal.