A review on Speech and Speaker Authentication System using Voice Signal feature selection and extraction

This paper discusses a speech-and-speaker (SAS) identification system. The speech signal is recorded and then processed. The speech signal is treated graphically in order to extract the essential image features as a basic step in successful data mining applications in the biometric techniques. The object considered here is the human-voice signal. The identifying and classifying methods are performed with Burg's estimation model and the algorithm of Toeplitz matrix minimal eigenvalues is used as the main tools for signal-image description and feature extraction. The extracted feature-carrying image comprises the elements of Toeplitz matrices to consecutively compute their minimal eigenvalues and introduce a set of feature vectors within a class of voices. At the stage of classification, both conventional and neural-network-based methods are used. This helps in speech recognition and speaker authentication. Some examples on applications and comparisons are presented. The required computations were performed in Matlab proving speech-signal image recognition in a simple and easy-to-use way. any special hardware and can be used along with other biometric technologies in hybrid systems for multi-factor verification.