Voice Activity Detection with Adaptive Threshold Algorithm

To improve speaker recognition performance in noisy environments, we propose a new Speech Activity Detection (SAD) algorithm using an adaptive threshold approach based on energy and zero-crossing rate analysis. This algorithm determines speaker activity states by analyzing speech signal energy and zero-crossing rates, while adaptively adjusting thresholds according to environmental noise levels to enhance recognition accuracy and stability. The core implementation involves calculating short-term energy using frame-based summation of squared amplitudes and detecting zero-crossing rates through sign change counting per audio frame.

Our algorithm leverages two critical speech features - energy and zero-crossing rate - to better distinguish between speaker activity and background noise through precise computational analysis. Unlike traditional static threshold methods, our adaptive threshold algorithm dynamically adapts to varying noise conditions using a rolling window statistics approach, providing more reliable speaker recognition results. The threshold adaptation logic employs exponential moving averages for noise floor estimation and implements hysteresis thresholding to prevent state oscillation.

Furthermore, the algorithm incorporates optimization strategies such as noise estimation and adaptive filtering to enhance performance. Through noise estimation and modeling using minimum statistics during non-speech segments, we achieve more accurate noise suppression. The adaptive filter component utilizes LMS (Least Mean Squares) or NLMS (Normalized LMS) algorithms to automatically adjust filter coefficients based on environmental noise characteristics, thereby improving recognition stability. Code implementation typically involves separate modules for feature extraction, noise profile updating, and decision logic with configurable adaptation parameters.

In summary, our novel Speech Activity Detection (SAD) algorithm employing adaptive thresholds based on energy and zero-crossing rate analysis significantly improves speaker recognition performance in noisy environments. The algorithm achieves this through precise speech feature computation, optimization strategies for environmental adaptation, and robust implementation that delivers more reliable and accurate recognition outcomes. The complete system can be implemented using frame-based processing with typical frame sizes of 20-30ms and overlap-add reconstruction for real-time applications.