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A Novel Approach for Text-Independent Speaker Identification Using Artificial Neural Network

Md. Monirul Islam, FahimHasan Khan, AbulAhsan Md. Mahmudul Haque

This article presents the implementation of Text Independent Speaker Identification system. It involves two parts- “Speech Signal Processing” and “Artificial Neural Network”. The speech signal processing uses Mel Frequency Cepstral Coefficients (MFCC) acquisition algorithm that extracts features from the speech signal, which are actually the vectors of coefficients. The backpropagation algorithm of the artificial neural network stores the extracted features on a database and then identify speaker based on the information. The direct speech does not work for the identification of the voice or speaker. Since the speech signal is not always periodic and only half of the frames are voiced, it is not a good practice to work with the half voiced and half unvoiced frames. So the speech must be preprocessed to successfully identify a voice or speaker. The major goal of this work is to derive a set of features that improves the accuracy of the text independent speaker identification system.