Voice Activity Detection (VAD) Algorithm for Speech Activity Recognition

Voice Activity Detection (VAD) algorithm is designed to identify active speech segments and non-active periods within audio signals. This algorithm enables the extraction of meaningful speech components for subsequent processing and analysis tasks. Implementation typically involves signal processing techniques such as energy thresholding, zero-crossing rate analysis, or machine learning approaches using spectral features. Key functions may include frame-based processing using Hamming windows, feature extraction (e.g., MFCCs), and classification mechanisms. Additionally, comprehensive technical literature and research papers are available that provide deeper insights into VAD principles, application domains, and recent advancements. These resources cover algorithmic foundations including probabilistic models, neural network implementations, and real-time processing optimizations. For researchers and engineers working with speech processing systems, these references offer valuable guidance for algorithm selection, parameter tuning, and performance evaluation metrics.