Dynamic Time Warping (DTW) Algorithm for Speech Signal Processing

This document presents the Dynamic Time Warping (DTW) algorithm and its applications in speech signal processing. The DTW algorithm is a fundamental technique in speech recognition that measures similarity between two temporal sequences by non-linearly warping them in the time dimension. The implementation typically involves calculating a cost matrix and finding the optimal warping path through dynamic programming. The solution incorporates three core components: 1. Endpoint Detection: Utilizes energy-based thresholding and zero-crossing rate analysis to identify speech segments within audio signals, often implemented using frame-based processing with overlapping windows. 2. MFCC Feature Extraction: Implements Mel-Frequency Cepstral Coefficients extraction through pre-emphasis, framing, windowing, FFT, Mel-filterbank application, and DCT transformation to create compact spectral representations. 3. Dynamic Time Warping: Features an optimized DTW implementation with customizable distance metrics (typically Euclidean) and path constraints, allowing adjustable warping window sizes for computational efficiency. These integrated modules form a complete pipeline for speech analysis and recognition tasks. Users can directly employ the provided algorithms or customize them through parameter adjustment and algorithm modification. The codebase employs modular design with clear function interfaces for easy extension. Note: Effective utilization requires foundational knowledge in digital signal processing mathematics and programming proficiency. Recommended prerequisites include understanding of linear algebra, probability theory, and hands-on experience with Python/Matlab signal processing libraries before implementation.