Speech Endpoint Detection Based on EMD and Higher Order Statistics in Noisy Environments
- DOI
- 10.2991/icimm-15.2015.199How to use a DOI?
- Keywords
- Endpoint Detection; EMD; Higher Order Statistics; Noisy Environments
- Abstract
Accurate endpoint detection is crucial for speech recognition accuracy. This paper presents a new technique for speech endpoint detection in a noisy environment based on the empirical mode decomposition (EMD) algorithm and higher order statistics. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then higher order statistics of the IMF components can be used to extract the desired feature for endpoint detection. In order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experimental results show that the performance of the proposed algorithm is noticeable in the real speech signal tests with different SNR.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Dexiang Zhang AU - Jiaxing Li AU - Zihong Chen PY - 2015/07 DA - 2015/07 TI - Speech Endpoint Detection Based on EMD and Higher Order Statistics in Noisy Environments BT - Proceedings of the 5th International Conference on Information Engineering for Mechanics and Materials PB - Atlantis Press SP - 1101 EP - 1104 SN - 2352-5401 UR - https://doi.org/10.2991/icimm-15.2015.199 DO - 10.2991/icimm-15.2015.199 ID - Zhang2015/07 ER -