Voice Recognition System with Speech Signal Endpoint Detection Capability

This research aims to provide speech signal endpoint detection functionality for voice recognition systems. Our study involves comprehensive investigation and analysis of speech signal endpoint detection, with particular emphasis on detecting boundaries within speech intervals. By analyzing speech signal characteristics and patterns, we developed an innovative algorithm that employs frame-based processing with overlapping windows to achieve precise endpoint detection. The algorithm utilizes short-term energy calculation combined with spectral centroid analysis to identify speech segments effectively. We further optimized and refined this algorithm by incorporating adaptive threshold techniques and noise robustness improvements to enhance its performance and accuracy. The implementation includes functions for signal preprocessing, feature extraction, and decision logic that can handle various speech environments. Complete software source code is provided for reference and practical application, featuring modular design with configurable parameters for different speech recognition scenarios.