A fuzzy adaptive smoothing approach to robust endpoint detection based on MDL using sub-band speech  

作　　者：王明政 张文军 李建华 诸鸿文 

机构地区：[1]Electronic Engineering Department, Shanghai Jiaotong University, Shanghai 200030, China

出　　处：《Journal of Harbin Institute of Technology(New Series)》2005年第6期705-709,共5页哈尔滨工业大学学报（英文版）

基　　金：SponsoredbytheHighTechResearchAndDevelopmentProgramme(GrantNo.2002AA145090).

摘　　要：To develop a more robust endpoint detection algorithm, this paper first proposes a fuzzy adaptive smoothing algorithm. The general idea underlying adaptive smoothing is to adapt the short-term sub-band mean of the amplitude to the local attributes of speech on the basis of discontinuity measures. The adaptive smoothing algorithm in this paper utilizes a scale-space framework through the minimal description length （MDL）. We recommend using the fuzzy muhi-attribute decision making approach to select the proper sub-bands where the word boundary can be more reliably detected. The process and simulation of the fuzzy adaptive smoothing algorithm are given. The parameters utilize the mean amplitude of the audible frequency range （300 -3 700 Hz） and the sub-band mean of the amplitude （16 band filter-bank）. We selected the audible band energy because of its usefulness in detecting high-energy regions and making the distinction between speech and noise. Otherwise, the fuzzy adaptive smoothing algorithm is processed in sub-band speech to utilize the full range of frequency information.To develop a more robust endpoint detection algorithm, this paper first proposes a fuzzy adaptive smoothing algorithm. The general idea underlying adaptive smoothing is to adapt the short-term sub-band mean of the amplitude to the local attributes of speech on the basis of discontinuity measures. The adaptive smoothing algorithm in this paper utilizes a scale-space framework through the minimal description length (MDL). We recommend using the fuzzy multi-attribute decision making approach to select the proper sub-bands where the word boundary can be more reliably detected. The process and simulation of the fuzzy adaptive smoothing algorithm are given. The parameters utilize the mean amplitude of the audible frequency range (300-3 700 Hz) and the sub-band mean of the amplitude (16 band filter-bank). We selected the audible band energy because of its usefulness in detecting high-energy regions and making the distinction between speech and noise. Otherwise, the fuzzy adaptive smoothing algorithm is processed in sub-band speech to utilize the full range of frequency information.

关 键 词：ROBUSTNESS endpoint detection sub-band SMOOTHING MDL（ minimal description length） 

分 类 号：TN912.3[电子电信—通信与信息系统]