Using Gradient Descent Method for Unconstrained Optimization Solutions

During speech signal processing, several crucial stages must be implemented including preprocessing, feature extraction, vectorization, and matching calculations. The preprocessing phase typically involves filtering unwanted noise, framing the signal into short segments, applying window functions (like Hamming window) to reduce spectral leakage, and performing endpoint detection to identify speech boundaries. In MATLAB implementation, filtering can be achieved using functions like `filter()` or `filtfilt()` for zero-phase filtering. Framing requires calculating appropriate frame size and overlap using sample rate considerations, while windowing applies functions such as `hamming()` or `hanning()` to each frame. Endpoint detection algorithms often utilize energy thresholds and zero-crossing rates, implementable through conditional statements and moving average calculations. For enhanced speech processing capabilities, the Voicebox toolbox (downloadable online) provides specialized functions like `vad()` for voice activity detection and `enframe()` for efficient framing operations. When processing speech signals, each step must be carefully designed with appropriate parameter selection based on specific requirements to ensure accurate and reliable results. Key considerations include choosing optimal frame durations (typically 20-30ms), overlap ratios (commonly 50%), and threshold values for endpoint detection based on signal characteristics.