MATLAB Source Code for DTW (Dynamic Time Warping) Speech Recognition Algorithm

This document introduces the DTW (Dynamic Time Warping) algorithm for speech recognition and provides its MATLAB source code implementation. DTW is a widely-used algorithm in speech recognition that enables accurate identification and understanding of speech signals. The algorithm's core functionality lies in its ability to dynamically align and compare speech patterns of different lengths through optimal pathfinding in a cost matrix. The MATLAB implementation demonstrates key components including: 1) Feature extraction using MFCC (Mel-Frequency Cepstral Coefficients) for speech parameterization, 2) Dynamic programming approach for calculating the optimal warping path, 3) Distance measurement between test and reference templates using Euclidean distance, and 4) Path normalization for length-invariant comparison. In the provided code, you will observe how to: initialize speech templates, compute the cumulative cost matrix using recursive dynamic programming, backtrack to find the optimal alignment path, and calculate the final similarity score. The implementation includes validation tests demonstrating DTW's effectiveness in handling temporal variations in speech patterns. This resource aims to facilitate deeper understanding of DTW's application in speech recognition systems, showcasing practical MATLAB coding techniques for pattern matching and time-series alignment in audio processing applications.