Isolated Word Speech Recognition with MATLAB Implementation

This MATLAB-implemented program performs isolated word speech recognition through sophisticated audio processing techniques. The system incorporates Mel-frequency cepstral analysis (MFCC) where it utilizes a Mel-filter bank to simulate human auditory perception characteristics. The implementation typically involves framing the audio signal, applying windowing functions (like Hamming windows), computing Fourier transforms, and processing through triangular Mel-scale filters to extract cepstral coefficients that represent spectral features. For individual word recognition, the program employs both time-domain and frequency-domain analysis to extract distinctive acoustic features. The core algorithm compares these extracted features against pre-trained speech models using pattern matching techniques such as Dynamic Time Warping (DTW) or Hidden Markov Models (HMMs) to determine recognition outcomes. Key functions likely include audio preprocessing, feature extraction modules, and pattern classification algorithms. This comprehensive approach enables accurate speech signal identification, making the program highly valuable for practical applications in voice-controlled systems and audio pattern recognition research.