DocumentCode
257961
Title
A non-intrusive PESQ measure
Author
Sharma, Dushyant ; Meredith, Lisa ; Lainez, Jose ; Barreda, Daniel ; Naylor, Patrick A.
Author_Institution
Voicemail-To-Text Res., Nuance Commun. Inc., Marlow, UK
fYear
2014
fDate
3-5 Dec. 2014
Firstpage
975
Lastpage
978
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.
Keywords
mean square error methods; regression analysis; signal processing; speech processing; trees (mathematics); NISQ; PESQ score; RMS error; binary tree regression based model; data-driven nonintrusive speech quality measure; feature extraction; intrusive PESQ algorithm; nonintrusive PESQ measure; speech data; speech signal; test database; training method; Databases; Quality assessment; Signal processing algorithms; Speech; Speech processing; Training; CART; Non-Intrusive; PESQ; Speech Quality;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on
Conference_Location
Atlanta, GA
Type
conf
DOI
10.1109/GlobalSIP.2014.7032266
Filename
7032266
Link To Document