The human voice is comprised of sound made by a human being using the vocal cord for talking,singing, laughing, crying and shouting. It is particularly a piece of human sound creation inwhich the vocal cord is the essential sound source, which play an important role in the conversation. The applications of speech or voice processing technology play a crucial role in humancomputer interaction. The system improves gender identification, age group classification, ageand emotion recognition performance. The research work uses new and efficient methods forfeature extraction of speech or voice and classification of standard method on the various audiodatasets. Mel Frequency Cepstral Coefficients feature extraction and selection is performed tofind a more suitable feature set for building speaker models. The proposed system uses GaussianMixture Model is a supervector for system feature selection and feature modelling. SupportVector Machine classification and feature matching technique is used to classify the featurefor different age groups like child, teenage, young, adult and senior to increase the resultantperformance and accuracy. The database is created using the audio files for each age group ofspeaker and for each emotion as an input, performs feature extraction and identifies the gender,classify age group, recognize age and emotion.

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Mel Frequency Cepstral Coefficient (mfcc) Gaussian Mixture Model (gmm) support Vector Machine (svm) Expectation-maximization (em) Maximum A Posteriori (map) Hidden Markov Models (hmms) Suprasegmental Hidden Markov Models (sphmms) Interactive Voice Response System (ivrs).