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Speaker Verification technology is crucial in identifying individuals through their unique voice characteristics. However, the increasing use of speech assistants has posed new challenges for this technology. To address this, transfer learning is employed using the ECAPA-TDNN model trained on human voices from the VoxCeleb2 dataset. This study compares intra-voice assistant variations and proceeds to inter-voice assistant comparisons. In intra-pair comparisons, text-independent samples achieved accuracies of 83.33% (iOS versions) and 66.67% (Alexa versions), while text-dependent samples achieved 50% accuracy for both versions. In inter-pair comparisons (Alexa, Siri, Google Assistant, Cortana), accuracies of 100% (text-independent) and 80% (text-dependent) were observed. These findings showcase the effectiveness of transfer learning and the ECAPA-TDNN model for secure speaker verification in …