Implementation of MFCC-Based Speech Recognition System Simulation

This paper presents a complete simulation of a speech recognition system utilizing Mel-Frequency Cepstral Coefficients (MFCC) technology. The implementation includes comprehensive source files required for speech recognition training. Our approach involves MFCC feature extraction through frame blocking, windowing, Fast Fourier Transform (FFT), Mel-filterbank processing, and discrete cosine transform (DCT) to capture speech characteristics. The system employs Gaussian Mixture Models (GMM) or Hidden Markov Models (HMM) for pattern recognition and classification. The provided source files contain training datasets, feature extraction algorithms, and machine learning models, enabling researchers to reproduce results and enhance recognition performance through parameter optimization. Through this simulation framework, we demonstrate fundamental speech recognition principles and methodology, contributing to the advancement of future speech technology developments. The code structure includes modular components for pre-processing, feature extraction, model training, and recognition testing, facilitating extensibility and performance benchmarking.