Speech Signal Short-Time Spectrum and Autocorrelation Function Analysis

This MATLAB program offers comprehensive speech signal analysis capabilities through efficient implementation of short-time spectrum analysis, autocorrelation functions, and zero-crossing rate calculations. The code architecture employs frame-based processing with configurable window functions (e.g., Hamming window) and overlap-add techniques for accurate spectral analysis. The autocorrelation module utilizes fast computation methods for pitch detection, while the zero-crossing rate algorithm implements robust threshold handling for voiced/unvoiced classification. The program features modular design with clearly commented functions, making it accessible for beginners learning speech processing fundamentals while providing sufficient flexibility for experienced users to modify parameters like frame size, window length, and analysis thresholds. Each module includes error checking and validation routines to ensure reliable operation across different sampling rates and signal qualities. Beyond basic analysis, the implementation demonstrates practical applications in feature extraction for speech recognition systems, with optimized memory management for handling long-duration audio files. The visualization components generate professional-grade plots for time-domain waveforms, spectral characteristics, and statistical features, enabling deeper insights into speech signal properties and patterns. This toolbox serves as a valuable resource for speech processing education and research, combining theoretical accuracy with practical implementation considerations through properly vectorized operations and efficient signal processing techniques.