Speaker Recognition System Based on Mel Frequency Cepstral Coefficient and Four Features

Authors

• Ashraf Tahseen, Department of Computer Science, University of Technology- Iraq, Baghdad, IraqFollow
• Hasanen S., Department of Computer Science, University of Technology- Iraq, Baghdad, IraqFollow
• Mohammad N. Fadhil, Department of Computer Science, University of Technology- Iraq, Baghdad, IraqFollow

Document Type

Original Study

Keywords

Computer Engineering

Abstract

Biometrics signs are the most important factor in the human recognition field and considered an effective technique for person authentication systems. Voice recognition is a popular method to use due to its ease of implementation and acceptable effectiveness. This original_study will introduce a speaker recognition system that consists of preprocessing techniques to eliminate noise and make the sound smoother. For the feature extraction stage, the method Mel Frequency Cepstral Coefficient (MFCC) is used, and in the second step, the four features (FF) Mean, Standard Division, Zero-Cross and Amplitude, which added to (MFCC) to improve the results. For data representation, vector quantization has been used. The evaluation method (k-fold cross-validation) has been used. Supervised machine learning (SML) is proposed using Quadratic Discriminant Analysis (QDA) classification algorithms. And the results obtained by the algorithm (QDA) varied between 98 percent and 98.43 percent, depending on the way of features extraction that was used. These results are satisfactory and reliable.

How to Cite This Article

Tahseen, Ashraf; S., Hasanen; and Fadhil, Mohammad N. (2021) "Speaker Recognition System Based on Mel Frequency Cepstral Coefficient and Four Features," Iraqi Journal of Computers, Communications, Control and Systems Engineering: Vol. 21: Iss. 4, Article 8.
DOI: 10.33103/uot.ijccce.21.4.8
Available at: https://ijccce.researchcommons.org/journal/vol21/iss4/8