Isolated Word Speech Recognition Using DTW Algorithm

The isolated word speech recognition program based on Dynamic Time Warping (DTW) can be implemented in the MATLAB environment. DTW is a widely-used speech recognition technique that measures similarity between two audio signals by finding the optimal alignment path between their feature sequences, typically using Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction. This program specializes in recognizing isolated words where there is no contextual relationship between utterances. The MATLAB implementation typically involves key functions like mfcc() for feature extraction, dtw() for calculating the warping path distance, and custom functions for template matching and threshold-based decision making. By employing the DTW algorithm, which dynamically time-normalizes utterances of different lengths, the system achieves higher recognition accuracy while minimizing false positives. The MATLAB environment provides excellent convenience for running this program, offering comprehensive toolboxes for audio processing, signal analysis, and matrix operations that streamline feature extraction and pattern comparison tasks. The implementation generally follows these steps: audio preprocessing (framing, windowing), feature extraction using MFCC, template database creation, and real-time matching using DTW distance calculation. Therefore, this DTW-based isolated word speech recognition program serves as a highly practical tool for speech processing applications and pattern recognition research.