Speech Frame Analysis: Windowing, Short-Time Energy, Zero-Crossing Rate, and Endpoint Detection

In this article, we demonstrate how to use MATLAB to plot Hanning, Hamming, and Blackman windows on the same graph for comparative analysis. The implementation involves creating window functions using MATLAB's built-in commands like hanning(), hamming(), and blackman(), followed by plotting them with distinct line styles for clear visualization. Furthermore, we explore essential speech processing techniques including frame segmentation where continuous speech signals are divided into short overlapping frames (typically 20-30ms duration). Each frame undergoes windowing operations to reduce spectral leakage, calculated using window functions applied to segmented data. We then compute short-time energy using frame-wise summation of squared signal samples, and short-time zero-crossing rate by counting sign changes within each frame - both serving as fundamental features for speech analysis. The process culminates in endpoint detection algorithms that utilize energy and zero-crossing thresholds to identify speech segments within audio signals. This comprehensive approach enables more effective analysis and processing of speech signals for applications in speech recognition and digital signal processing.