MATLAB Implementation of Simple Endpoint Detection

Implementation Approach for Simple Endpoint Detection Endpoint detection is a fundamental task in speech processing used to determine the start and end positions of speech signals. The core logic of the dual-threshold decision method based on zero-crossing rate and short-term energy is described below: Short-term Energy Analysis Short-term energy reflects intensity changes in the signal, typically calculated by framing the speech signal and computing energy values for each frame. In MATLAB implementation, this can be achieved using frame-by-frame processing with functions like 'buffer' for segmentation and summing squared samples within each frame. Frames with higher energy likely correspond to speech segments, while low-energy frames may represent silence or background noise. Zero-Crossing Rate Calculation Zero-crossing rate indicates the frequency at which the signal crosses zero, used to distinguish between unvoiced sounds (high frequency) and voiced sounds (low frequency). The implementation involves counting sign changes between consecutive samples within each frame using logical operations. Silence and voiced sounds exhibit lower zero-crossing rates, while unvoiced sounds or noise show higher rates. Dual-Threshold Decision Combining short-term energy and zero-crossing rate with high and low thresholds: Preliminary detection: When energy or zero-crossing rate exceeds the high threshold, mark as potential speech segments using conditional statements and flag arrays. Confirmation phase: Within initially detected intervals, if consecutive frames exceed the low threshold, confirm as speech segments using frame counting and validation loops; otherwise eliminate false detections. Post-processing Optimization Smooth the detected endpoints to avoid misjudgment caused by transient noise. This can be implemented using morphological operations or logical filters to merge adjacent speech segments that are too close and remove isolated segments that are too short. This method can be implemented in MATLAB through framing, frame-by-frame feature calculation, and logical decision-making, suitable for entry-level speech processing tasks. For complex environments (such as high noise), additional techniques like spectral entropy or machine learning methods need to be incorporated to enhance robustness. Key MATLAB functions involved may include signal framing, statistical calculations, and threshold-based classification algorithms.