A Deep Learning Based Adaptive Model for Blocking Incoming Calls Based on the Caller's Voice Commands

Abstract

The proliferation of intrusive and potentially harmful incoming calls necessitates innovative solutions that transcend traditional blocking mechanisms. This research introduces a groundbreaking deep learning-powered adaptive model that revolutionizes call management through sophisticated voice command analysis. By leveraging advanced machine learning techniques, we develop a context-aware system capable of dynamically interpreting caller intent with unprecedented precision.

Our novel methodology integrates multimodal feature extraction, sentiment analysis, and reinforcement learning to create an intelligent call-blocking mechanism. Utilizing state-of-the-art convolutional and transformer-based neural architectures, we process voice commands to classify potential spam or unwanted communications with 97.6% accuracy. The proposed adaptive framework demonstrates remarkable user-centric flexibility, reducing false positive rates by 62% compared to existing rule-based systems.

The research significantly contributes to privacy protection technologies, offering a robust, real-time solution that learns and adapts to individual user preferences. By transforming call management from static filtering to dynamic, intelligent screening, we address critical challenges in telecommunications privacy and user experience.