Speaker Identification for Low-End Devices: A Secure Voice Biometric Solution for Mobile Banking

The rise of the mobile era, especially with the availability of the internet, has significantly contributed to the growth of mobile banking, enhancing financial inclusion, particularly among underserved populations. However, reliance on basic mobile devices without advanced biometric security features exposes users to risks of financial fraud, unauthorized account access, and identity theft. This study introduces a lightweight speaker identification system tailored to improve the security of mobile banking in low-resource environments. By utilizing voice biometric authentication, the system confirms the user's identity through their voice at the end of each transaction, ensuring that only the authorized user can complete the transaction even if their Personal Identification Number (PIN) is compromised. The system combines advanced deep learning models, such as Whisper-large-v3 and Emphasized Channel Attention, Propagation, and Aggregation in Time Delay Neural Network (ECAPA-TDNN), to extract detailed voice features, including linguistic patterns and unique speaker traits, ensuring high accuracy and reliability in real-time identification. Additionally, liveness detection is integrated to defend against advanced spoofing, further enhancing system security. Evaluations on real-world data demonstrate the system's robust performance, achieving 99.59% accuracy, 99.62 % precision, and an Equal Error Rate (EER) of 0.0017, validating the system's practicality in constrained environments without requiring extensive computational resources. This solution provides a scalable and secure voice authentication method that is particularly beneficial for underserved communities, advancing both mobile banking security and financial inclusion.