A Speech Endpoint Detection Based on Empirical Mode Decomposition and Average Magnitude Difference Function

Abstract:

Speech endpoint detection plays an important role in speech signal processing. In this paper, a method of speech endpoint detection based on empirical mode decomposition is introduced for accurately detecting the speech endpoint. This method used in speech signal decomposition gets a set of intrinsic mode functions (IMF). An IMF which contained a lot of noise must be filtered, and the rest of IMFs can be reconstructed to a new speech signal. The speech endpoint is detected by average magnitude difference function precisely. Simulation experiments show that the method proposed in this paper can eliminate the impact of noise effectively and detect the speech signal endpoint accurately.

• Periodical: Applied Mechanics and Materials (Volumes 325-326)
• Pages: 1649-1652
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.325-326.1649
• Online since: June 2013
• Authors: Wei Wei Shi, Wei Hua Xiong, Yun Yun Chu, Yu Liu
• Keywords: Average Magnitude Difference Function, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF), Signal-to-Noise Ratio (SNR), Speech Endpoint Detection

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