Analyzing Speech Signal Processing Using MATLAB Tools

In this document, we conduct detailed experimental analysis of several speech signal processing aspects using MATLAB tools. We first examine the computational methodology for short-time energy, demonstrating how to implement frame-based energy calculation using MATLAB's buffer function and element-wise operations. The application of short-time energy in speech activity detection and segmentation will be discussed with practical code examples. Subsequently, we introduce endpoint detection concepts and algorithms, focusing on dual-threshold methods that combine energy and zero-crossing rate features. The implementation will include techniques for accurately identifying speech onset and offset points using moving window analysis. Next, we delve into power spectrum analysis and short-time Fourier transform characteristics, explaining how to use MATLAB's spectrogram function with proper windowing parameters (Hamming/Hann windows) and overlap settings. The significance of these spectral analysis techniques in feature extraction for speech recognition and speaker identification will be thoroughly discussed. Through these comprehensive analyses with code-oriented explanations, we aim to provide deeper understanding of speech signal processing fundamentals and their practical implementation.