%0 Journal Article
%T Novel Model Compensation Based on Non-uniform Spectral Compression Features<br>基于非均匀谱压缩特征的模型补偿新算法
%A Ning Geng-xin
%A Wei Gang
%A Kong Xiang-zhu
%A <br>宁更新
%A 韦岗
%A 孔祥祝
%J 电子与信息学报
%D 2007
%I 
%X A novel model compensation method is proposed, which integrates the Vector Taylor Series (VTS) approach with a robust feature extraction technique called SNR-dependent Non-uniform Spectral Compression (SNSC). The SNSC method is a spectral operation of magnitude transformation which resembles the human intensity-to-loudness conversion process and de-emphasizes noisy bands. Based on this mismatch function, which models the effect of the noise onto the clean speech in the Log-spectral domain together with the SNSC, a new model compensation procedure is derived. By adopting this novel model compensation approach, significant improvement over the PMC and VTS method can be found in different additive noisy environments at the expense of slight increase in computational complexity.
%K Speech recognition
%K Model compensation
%K Non-uniform spectral compression<br>语音识别
%K 模型补偿
%K 非均匀谱压缩
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=B9BC779EC962B2A6&yid=A732AF04DDA03BB3&vid=771469D9D58C34FF&iid=B31275AF3241DB2D&sid=7A60741D2B519BE0&eid=77586D59F496BF38&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=10