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Document Type : Latin Dissertation
Language of Document : English
Record Number : 148587
Doc. No : ET20379
Main Entry : Narendranat h Malayat h
Title Proper : Data-Driven Methods for Extracting Features from Speech
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : Feature extraction plays a major role in any form of pattern recognition- Currentfeature extraction methods used for automatic speech recognition (ASR) and speakerverification rely mainly on properties of speech production (modeled by d-pole filters)and perception (critical band integration simulated by Mel/Bark filter bank). We proposestochastic methods t o design feature extraction methods which are trained t o alleviate theunwanted variability present in speech signal, In this dissertation we show that such data-driven met hods provide significant advantages over the conventional met hods for featureextraction..
Subject : Electericl tess
: برق
electronic file name : TL43506.pdf
Title and statement of responsibility and : Data-Driven Methods for Extracting Features from Speech [Thesis]
 
 
 
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