Endpoint Detection Using Short-Term Energy and Spectral Entropy

This research presents a preliminary investigation into endpoint detection using short-term energy and spectral entropy analysis. The study explores how these signal characteristics can be employed to identify the start and end points of speech signals. Accurate endpoint detection is crucial for numerous speech processing applications, including speech recognition, speech synthesis, and speech enhancement. Our approach involves implementing digital signal processing techniques such as windowing functions (e.g., Hamming window) to compute frame-based short-term energy and spectral entropy features. The algorithm typically involves dividing the speech signal into overlapping frames, calculating energy values using squared magnitude summation, and computing spectral entropy through Fast Fourier Transform (FFT) and probability distribution analysis of power spectrum components. We plan to employ machine learning algorithms, potentially using Python libraries like scikit-learn or TensorFlow, to train classification models that can automatically detect signal endpoints based on these extracted features. This research aims to contribute novel methodologies and insights to the field of speech signal processing.