Application of Spectral Variance in Speech Signal Endpoint Detection with MATLAB Implementation

In speech signal endpoint detection, spectral variance serves as a fundamentally practical concept for identifying voice activity boundaries. By computing the variance across frequency bands in the signal spectrum, we can accurately determine start and end points of speech segments. This technique proves crucial in various speech processing applications including speech recognition systems, voice synthesis engines, and audio segmentation algorithms.

The MATLAB implementation typically involves these key steps: First, preprocess the audio signal using framing and windowing techniques (Hamming/Hanning windows) to prepare for spectral analysis. Then apply Fast Fourier Transform (FFT) to convert time-domain signals into frequency-domain representations. The core algorithm calculates variance across frequency bins for each frame, often using magnitude spectrum values. Voice activation detection thresholds are then applied to these variance measurements to distinguish speech from silence or noise.

Key MATLAB functions employed include: fft() for spectral transformation, var() for variance computation, and envelope detection algorithms for boundary identification. The program may incorporate adaptive thresholding techniques that dynamically adjust to noise conditions, enhancing robustness in real-world environments.

Mastering spectral variance applications provides significant benefits for developing accurate voice activity detectors. This document introduces the theoretical foundation of spectral variance and delivers a practical MATLAB program demonstrating its implementation with commented code examples and parameter optimization guidelines.