Development of an English Speech Privacy Protection Model Leveraging Biotechnology Innovations for Business Applications

Weigang Wang
School of Foreign Language Studies, Weinan Normal University, Weinan 714099, Shaanxi, China

DOI:https://doi.org/10.5912/jcb2520

Abstract:

The protection of speech data privacy has emerged as a critical concern in biotechnology-driven commercial applications, particularly in sectors such as voice biometric authentication and telemedicine. This study introduces an advanced English speech privacy protection model grounded in self-supervised learning, designed to enhance secure biometric data management within commercial biotechnology environments. The model utilizes sophisticated speech transformation techniques to convert raw speech data into quantifiable GhostVec representations, subsequently generating synthetic waveforms optimized for integration into biometric systems. Experimental validation using the LibriSpeech test-clean dataset focused on two key dimensions: privacy preservation effectiveness and system operational efficiency. Results indicate that the proposed model achieves state-of-the-art privacy protection while maintaining a real-time processing coefficient 0.2% higher than the established McAdams method. This advancement not only strengthens data security compliance for biotechnology enterprises but also minimizes computational overhead in commercial deployment scenarios. From a business perspective, the model offers a scalable and efficient solution for managing speech biometrics in voice-based authentication systems, telehealth platforms, and smart agriculture monitoring devices. By integrating self-supervised learning with synthetic speech generation, this research establishes a novel framework for balancing privacy preservation with biometric utility, contributing to the advancement of secure biotechnology-driven commercial ecosystems.