Feature Fusion for Performance Enhancement of Text Independent Speaker Identification

Zahra Shah; Giljin Jang; Adil Farooq

doi:10.62762/TIS.2024.649374

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Volume 2, Issue 1, IECE Transactions on Intelligent Systematics

Volume 2, Issue 1, 2024

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IECE Transactions on Intelligent Systematics, 2024, Volume 2, Issue 1: 27-37

Free Access | Research Article | 31 December 2024

Feature Fusion for Performance Enhancement of Text Independent Speaker Identification

Zahra Shah 1,2 *

Giljin Jang 1

Adil Farooq 3

1 School of Electronics Engineering, Kyungpook National University, Daegu 41566, South Korea

2 Sensify Inc., New York, NY 10016,USA

3 Department of Electronic Engineering, Maynooth University, W23 A3HY, Ireland

* Corresponding Author: Zahra Shah, [email protected]

DOI: 10.62762/TIS.2024.649374

Received: 16 October 2024, Accepted: 09 December 2024, Published: 31 December 2024

Abstract

Speaker identification systems have gained significant attention due to their potential applications in security and personalized systems. This study evaluates the performance of various time and frequency domain physical features for text-independent speaker identification. Specifically, four key features—pitch, intensity, spectral flux, and spectral slope—were examined along with their statistical variations (minimum, maximum, and average values). These features were fused with log power spectral features and trained using a Convolutional Neural Network (CNN). The goal was to identify the most effective feature combinations for improving speaker identification accuracy. The experimental results revealed that the proposed feature fusion method outperformed the baseline system by 8%, achieving an accuracy of 87.18%.

Graphical Abstract

Keywords

speaker identification

prosodic features

physical features

CNN

features fusion

Funding

This work was supported without any funding.

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APA Style

Shah, Z., Jang, G., & Farooq, A. (2024). Feature Fusion for Performance Enhancement of Text Independent Speaker Identification. IECE Transactions on Intelligent Systematics, 2(1), 27–37. https://doi.org/10.62762/TIS.2024.649374

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