Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44299
標題: Recurrent type-2 fuzzy neural network using Haar wavelet energy and entropy features for speech detection in noisy environments
作者: Tu, C.C.
莊家峰
Juang, C.F.
關鍵字: Fuzzy neural networks
Type-2 fuzzy systems
Recurrent fuzzy neural
networks
Wavelet transform
Speech detection
word boundary detection
inference network
robust algorithm
recognition
systems
期刊/報告no:: Expert Systems with Applications, Volume 39, Issue 3, Page(s) 2479-2488.
摘要: This paper proposes a new method to detect the boundary of speech in noisy environments. This detection method uses Haar wavelet energy and entropy (HWEE) as detection features. The Haar wavelet energy (HWE) is derived by using the robust band that shows the most significant difference between speech and nonspeech segments at different noise levels. Similarly, the wavelet energy entropy (WEE) is computed by selecting the two wavelet energy bands whose entropy shows the most significant speech/nonspeech difference. The HWEE features are fed as inputs to a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2ENN) for classification. The RSEIT2ENN is used because it uses type-2 fuzzy sets, which are more robust to noise than type-1 fuzzy sets. The recurrent structure in the RSEIT2FNN helps to remember the context information of a test frame. The RSEIT2ENN outputs are compared with a parameter threshold to determine whether it is a speech or nonspeech period. The HWEE-based RSEIT2ENN detection was applied to speech detection in different noisy environments with different noise levels. Comparisons with different detection methods verified the advantage of the proposed method of using HWEE. (C) 2011 Elsevier Ltd. All rights reserved.
URI: http://hdl.handle.net/11455/44299
ISSN: 0957-4174
文章連結: http://dx.doi.org/10.1016/j.eswa.2011.08.100
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