A Novel automatic voice recognition system based on text-independent in noisy environment

Motaz Hamza, Touraj Khodadadi, Sellappan Palaniappan

Abstract


Automatic voice recognition system aims to limit fraudulent access to sensitive areas as labs. Our primary objective of this paper is to increase the accuracy of the voice recognition in a noisy environment of the Microsoft Research (MSR) identity toolbox. The proposed system will ask the user to speak into the microphone then it will match an unknown voice with other human voices existing in the database using a statistical model, in order to grant or deny access to the system. Accordingly, voice recognition is done in two steps: training and testing. During the training, a Universal Background Model as well as a Gaussian Mixtures Model: GMM-UBM models are calculated based on different sentences pronounced by the human voice (s) used to record the training data. Then the testing of the voice signal in a noisy environment is done by calculating the Log Likelihood Ratio of the GMM-UBM models in order to classify the user's voice. However, before testing noise and de-noise methods are applied, as well as we investigate different MFCC features of the voice to determine the best feature possible as well as noise filter algorithm that will improve the performance of the automatic voice recognition system.

Keywords


Automatic Voice Recognition (AVR), Microsoft Research identity toolbox (MSR), Gaussian Mixture Model (GMM), Universal Background Model (UBM), Mel Frequency Cepstral Confections (MFCC’s)

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DOI: http://doi.org/10.11591/ijece.v10i4.pp%25p
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