VQ-Based Speaker-Dependent Isolated Word Speech Recognition

In this implementation, we employ a Vector Quantization (VQ)-based approach for speaker-dependent isolated word speech recognition. This method is designed to process pre-recorded audio files in .wav format through a systematic pipeline that includes feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs), codebook generation via the Linde-Buzo-Gray (LBG) algorithm, and pattern matching through distortion measurement. The core algorithm involves creating speaker-specific codebooks by clustering feature vectors, which enables accurate recognition of isolated words by comparing input speech patterns with stored templates. This advanced technique effectively converts spoken words into textual representations with high precision, making it suitable for applications such as speech recognition systems, voice assistants, and related domains. By implementing this approach with proper parameter tuning and codebook optimization, we can significantly enhance the processing and understanding of speech data, thereby improving both operational efficiency and recognition accuracy in real-world applications.