instance based sparse classifier fusion for speaker verification

  • سال انتشار: 1395
  • محل انتشار: فصلنامه سیستم های اطلاعاتی و مخابرات، دوره: 4، شماره: 3
  • کد COI اختصاصی: JR_JIST-4-3_007
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 511
دانلود فایل این مقاله

نویسندگان

Mohammad Hasheminejad

Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

Hassan Farsi

Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

چکیده

This paper focuses on the problem of ensemble classification for text-independent speaker verification. Ensemble classification is an efficient method to improve the performance of the classification system. This method gains theadvantage of a set of expert classifiers. A speaker verification system gets an input utterance and an identity claim, then verifies the claim in terms of a matching score. This score determines the resemblance of the input utterance and preenrolledtarget speakers. Since there is a variety of information in a speech signal, state-of-the-art speaker verification systems use a set of complementary classifiers to provide a reliable decision about the verification. Such a system receivessome scores as input and takes a binary decision: accept or reject the claimed identity. Most of the recent studies on the classifier fusion for speaker verification used a weighted linear combination of the base classifiers. The corresponding weights are estimated using logistic regression. Additional researches have been performed on ensemble classification byadding different regularization terms to the logistic regression formulae. However, there are missing points in this type of ensemble classification, which are the correlation of the base classifiers and the superiority of some base classifiers foreach test instance. We address both problems, by an instance based classifier ensemble selection and weight determination method. Our extensive studies on NIST 2004 speaker recognition evaluation (SRE) corpus in terms of EER, minDCF and minCLLR show the effectiveness of the proposed method.

کلیدواژه ها

Speaker Recognition; Speaker Verification; Ensemble Classification; Classifier Fusion; IBSparse

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.