Gender Recognition and Emotion Classification

this paper we implemented a gender recognition and emotion classification by voice. Speech signal processing have many application in recognition process or to identify whether a user is authorized or not. In speech recognition gender identification is important to identify or make certain application more secure. Signal is very according to their time, space etc. Signal is represented in the form of binary value i.e. 0 and 1. According to Webster through speech we can easily express our thoughts. It not only contain a signal but also contain a some valuable information about the speaker like age, gender ,their expression ,their state of mind or we can say that their mood. On the basis of speech we can also classify the mood of the speaker. In this project we use K-Means and Support Vector Machines (SVM) algorithm to classify opposing emotions. We separate the speech by speaker gender to investigate the relationship between gender and emotional content of speech. Here we extract a variety of temporal and spectral features from human speech. In this we use pitch relating statistics, Mel Frequency Cepstral Coefficients (MFCC) algorithms. The emotion recognition accuracy of these experiments is quite high accuracy as comparison to other algorithm. This process also allows us to develop criteria to class emotions together. In this algorithm our system accuracy is more high.