کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
559088 875048 2011 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification
چکیده انگلیسی

This paper presents an extended study on the implementation of support vector machine (SVM) based speaker verification in systems that employ continuous progressive model adaptation using the weight-based factor analysis model. The weight-based factor analysis model compensates for session variations in unsupervised scenarios by incorporating trial confidence measures in the general statistics used in the inter-session variability modelling process. Employing weight-based factor analysis in Gaussian mixture models (GMMs) was recently found to provide significant performance gains to unsupervised classification. Further improvements in performance were found through the integration of SVM-based classification in the system by means of GMM supervectors.This study focuses particularly on the way in which a client is represented in the SVM kernel space using single and multiple target supervectors. Experimental results indicate that training client SVMs using a single target supervector maximises performance while exhibiting a certain robustness to the inclusion of impostor training data in the model. Furthermore, the inclusion of low-scoring target trials in the adaptation process is investigated where they were found to significantly aid performance.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Speech & Language - Volume 25, Issue 2, April 2011, Pages 327–340
نویسندگان
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