Open Access

ARTICLE

Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

Uğur Ayvaz¹, Hüseyin Gürüler², Faheem Khan³, Naveed Ahmed⁴, Taegkeun Whangbo^3,*, Abdusalomov Akmalbek Bobomirzaevich³

1 Department of Computer Engineering, Istanbul Technical University, Istanbul, 34485, Turkey
2 Department of Information Systems Engineering, Mugla Sitki Kocman University, Mugla, 48000, Turkey
3 Artificial Intelligence Lab, Department of Computer Engineering, Gachon University, Seongnam, 13557, Korea
4 Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, 27272, UAE

* Corresponding Author: Taegkeun Whangbo. Email:

(This article belongs to the Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)

Computers, Materials & Continua 2022, 71(3), 5511-5521. https://doi.org/10.32604/cmc.2022.023278

Received 01 September 2021; Accepted 01 November 2021; Issue published 14 January 2022

Abstract

Automatic speaker recognition (ASR) systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals. One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients (MFCCs). Recent researches show that MFCCs are successful in processing the voice signal with high accuracies. MFCCs represents a sequence of voice signal-specific features. This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings. Since the human perception of sound is not linear, after the filterbank step in the MFCC method, we converted the obtained log filterbanks into decibel (dB) features-based spectrograms without applying the Discrete Cosine Transform (DCT). A new dataset was created with converted spectrogram into a 2-D array. Several learning algorithms were implemented with a 10-fold cross-validation method to detect the speaker. The highest accuracy of 90.2% was achieved using Multi-layer Perceptron (MLP) with tanh activation function. The most important output of this study is the inclusion of human voice as a new feature set.

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