Speech Endpoint Detection Based on EMD and Improved Spectral Subtraction

Aiming at the problem that the correct rate of speech endpoint detection is low in the environment with low signal-to-noise ratio, a speech endpoint detection algorithm based on Empirical Mode Decomposition (EMD) and improved spectral subtraction is proposed, considering some noise reduction before endpoint detection. After EMD decomposition and reconstruction, the algorithm uses the improved spectral subtraction of multi-window spectral estimation to reduce noise, which improves the signal-to-noise ratio of speech signal, and then detects the endpoint by using the Teager energy and Zero-Crossing Rate(ZCR). The effectiveness and feasibility of the method presented in this paper are verified by the simulation experiment. The speech signals selected in the experiment were recorded in a quiet environment. Compared with the speech endpoint detection algorithm based on empirical modal decomposition and improved two-threshold method, the proposed algorithm has significantly improved the accuracy and accuracy of endpoint detection.