D. Sharma, Lisa Meredith, Jose Lainez, Daniel Barreda, P. Naylor
{"title":"A non-intrusive PESQ measure","authors":"D. Sharma, Lisa Meredith, Jose Lainez, Daniel Barreda, P. Naylor","doi":"10.1109/GlobalSIP.2014.7032266","DOIUrl":null,"url":null,"abstract":"We present NISQ, a data-driven non-intrusive speech quality measure that has been trained to predict the PESQ score for a given speech signal. NISQ is based on feature extraction and a binary tree regression based model. A training method using the intrusive PESQ algorithm to automatically label large quantities of speech data is presented and utilized. Our method is shown to predict PESQ with an RMS error of 0.49 on our test database.","PeriodicalId":362306,"journal":{"name":"2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GlobalSIP.2014.7032266","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
We present NISQ, a data-driven non-intrusive speech quality measure that has been trained to predict the PESQ score for a given speech signal. NISQ is based on feature extraction and a binary tree regression based model. A training method using the intrusive PESQ algorithm to automatically label large quantities of speech data is presented and utilized. Our method is shown to predict PESQ with an RMS error of 0.49 on our test database.
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