Work

A Robust Deep Learning-Based Speaker Identification System Using Hybrid Model on KUI Dataset

Year: 2024

Type: article

Abstract: Background: Speaker identification, detecting human voices using speech characteristics and acoustics, is essential in security, biometrics, IoT, and human-computer interaction (HCI). As technology advances, more innovative software and robust hardware enhance these applications. This study evaluates feature extraction, pre-processing, and deep learning methods for speaker identification in natural settings. Methods: We compared deep learning algorithms, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a proposed Hybrid model. Audio files were processed using different feature extraction and pre-processing techniques. Results: The proposed Hybrid model achieved the highest accuracy at 95%, surpassing other models. LSTM followed with an accuracy of 93%. Performance metrics, including accuracy, recall, and F1 score, were used to evaluate the models. Conclusions: The study demonstrates that the Hybrid model is the most effective for speaker identification in natural settings, highlighting its potential for improved human-computer interaction and security applications.

Source: International Journal of Electrical and Electronics Research

DOI: 10.37391/ijeer.120446

Authors: Subrat Kumar Nayak, Ajit Kumar Nayak, Suprava Ranjan Laha, Nrusingha Tripathy, Takialddin AI Smadi

Institutions: Siksha O Anusandhan University, Brainware University, Jerash University

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4406161328

FWCI: 5.473

Cites: 25

Cited by: 36

Related to: 10