Speech Signal Endpoint Detection Simulation

Speech signal endpoint detection simulation represents a critical task in digital signal processing. This process requires calculating three key parameters: short-term energy (measuring signal amplitude variations), zero-crossing rate (quantifying frequency characteristics), and autocorrelation functions (detecting periodicity and pitch information). These computations are fundamental for accurately identifying the start and end points of speech segments. Through simulation experiments, developers can evaluate algorithmic performance by implementing frame-based processing with overlapping windows, typically using Hamming or Hanning window functions. The simulation enables comparative analysis of detection accuracy under various noise conditions and algorithmic approaches, providing valuable references for real-world speech processing applications. Research and development in this field require sophisticated MATLAB or Python implementations involving signal framing, feature extraction, and threshold optimization algorithms, making it both technically challenging and practically significant for speech recognition systems and audio processing applications.