Automated Speech Signal Recognition System

Automated speech signal recognition represents a crucial technology that converts speech signals into textual format. This system implements three distinct algorithmic approaches to achieve automatic recognition: Dynamic Time Warping (DTW) for handling time-series variations, Vector Quantization (VQ) for efficient feature compression, and Hidden Markov Models (HMM) for statistical pattern recognition. The MATLAB implementation includes key functions such as feature extraction using MFCC (Mel-Frequency Cepstral Coefficients), pattern matching algorithms, and probability modeling techniques. Each algorithm can be selectively applied based on specific requirements including recognition accuracy, computational efficiency, and real-time processing needs. To enhance user accessibility, we provide an interactive MATLAB graphical user interface (GUI) that simplifies the speech recognition workflow through intuitive controls for data input, algorithm selection, parameter configuration, and result visualization. The interface incorporates callback functions for seamless algorithm switching and real-time performance monitoring, making speech signal processing more accessible for both research and practical applications.