This study investigates the performance of various speaker recognition models, including SpeakerNet, TiTANet Small, and TiTANet Large, using the IITG-MV dataset for speaker verification tasks. The preprocessing steps involved resampling, noise reduction, segmentation, and volume normalization to prepare the audio data for input into the models. The models were evaluated based on their ability to correctly verify whether two audio samples belong to the same speaker or not. The evaluation metrics, derived from the confusion matrix, revealed that SpeakerNet outperformed both TiTANet variants, achieving an accuracy of 85%, while TiTANet Small and TiTANet Large achieved accuracies of 75% and 80%, respectively. Despite lacking graphical visualizations, the confusion matrix provided a comprehensive view of the models’ performance, showing how each model handled correct and incorrect speaker match predictions. The results highlight that SpeakerNet is the most effective model for speaker recognition in this setup, demonstrating superior accuracy and robustness in identifying speaker-specific features. These findings can guide future research in optimizing speaker recognition models for real-world applications involving speaker verification.