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

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.