Personal computer-based data collection and analysis systems may now be more resilient due to the recent advances in digital signal processing technology. The signal processing approach known as Speaker Recognition, uses the specific information contained in voice waves to automatically identify the speaker. For a single source, this study examines systems that can recognize a wide range of emotional states in speech. Since it offers insight into human brain states, it's a hot issue in the development during the interface between human and computer arrangement for speech processing. Mostly, it is necessary to recognize the emotional state of people in the arrangement. This research analyses an effort to discern various emotional stages such as anger, joy, neutral, fear and sadness by classification methods. The acoustic feature, a measure of unpredictability, is used in conjunction with a non-linear signal quantification approach to identify emotions. The unpredictability of all the emotional signals is included in a feature vector constructed from the calculated entropy measurements. In the next step, the acoustic features through speech signal are used for the training in the proposed neural network that are given to linear discriminator analysis approach for further greater classification with acoustic feature extraction. Besides, this research article compares the proposed work with various modern classifiers such as K- nearest neighbor, support vector machine and linear discriminator approach. Moreover, this proposed algorithm is based on acoustic features in Linear Discriminant Analysis (LDA) with acoustic feature extraction machine algorithm. The great advantage of this proposed algorithm is that it separates negative and positive features of emotions and provides good results during classification. According to the results from efficient cross-validation in the proposed framework, accessible sample of dataset of Emotional Speech, a single-source LDA classifier can recognize emotions in speech signals with above 90 percent of accuracy for various emotional stages.

Speech signal feature vector Linear Discriminant Analysis Machine learning algorithm acoustic feature extraction