VQ-Based Speaker Recognition System Implementation

In speech recognition systems, human-computer interface design constitutes a crucial aspect and represents a highly significant application technology in speech signal processing. Vector Quantization (VQ) serves as an exceptionally effective technique in isolated word recognition systems, particularly through Finite-State Vector Quantization (FSVQ) methods that substantially improve recognition accuracy. VQ-based isolated word recognition systems additionally offer advantageous comprehensive characteristics including minimal data storage requirements and rapid real-time response capabilities. Therefore, this course project requires the design of a speaker-dependent isolated word recognition system based on VQ technology. Using MATLAB programming tools, students must develop VQ codebook training programs and recognition algorithms to accurately identify specific voice commands such as "Up," "Down," "Left," and "Right." The primary implementation tasks include: 1. Designing a speaker-dependent isolated word recognition system utilizing VQ methodology, incorporating voice acquisition procedures and recognition detection experiments. This involves implementing voice activity detection (VAD) algorithms and preprocessing techniques including endpoint detection and noise reduction filters. 2. Developing MATLAB simulation programs with comprehensive system modulation and operational result analysis. The implementation should feature: - Feature extraction modules using Mel-Frequency Cepstral Coefficients (MFCC) with frame blocking and windowing functions - Codebook generation through Linde-Buzo-Gray (LBG) algorithm for vector quantization - Pattern matching mechanisms employing distance calculation functions (e.g., Euclidean distance, Manhattan distance) - Recognition decision logic based on minimum distance classifiers Throughout the course design process, students must conduct in-depth research on VQ technology and its practical applications, integrating MATLAB tools for system design and simulation experiments to achieve accurate speech signal recognition. The implementation should include performance evaluation metrics such as recognition accuracy rates and computational efficiency analyses.