Paper Title:
Voice Activity Detection with Decision Trees in Noisy Environments
  Abstract

An improved project based on double thresholds method in noisy environments is proposed for robust endpoints detection. Firstly, in this method, the distribution of zero crossing rate (ZCR) on the preprocessed signal is taken into account, and then the speech signal is divided into different parts to obtain appropriate thresholds with decision trees on the basis of the ZCR distribution. Finally, the double thresholds method, focusing on different importance of the energy and ZCR, is taken in the corresponding situation to determine the input segment is speech or non-speech. Simulation results indicate that the proposed method with decision trees obtains more accurate data than the traditional double thresholds method.

  Info
Periodical
Chapter
Chapter 3: Sensor, Test and Signal Processing
Edited by
Zhixiang Hou
Pages
749-752
DOI
10.4028/www.scientific.net/AMM.128-129.749
Citation
D. L. Hu, L. Z. Yi, Z. Pei, B. Luo, "Voice Activity Detection with Decision Trees in Noisy Environments", Applied Mechanics and Materials, Vols. 128-129, pp. 749-752, 2012
Online since
October 2011
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Price
$32.00
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