Sparse Classifier Fusion for Speaker Verification
نویسندگان
چکیده
منابع مشابه
Instance Based Sparse Classifier Fusion for Speaker Verification
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 the advantage 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 s...
متن کاملConfidence based multiple classifier fusion in speaker verification
A novel framework based on Bayes-based confidence measure for Multiple Classifier System fusion is proposed. Compared with ordinary Bayesian fusion, the presented approach leads to reductions of 20% and 25% in EER and ROC curve area, respectively, in speaker verification.
متن کاملPolynomial classifier techniques for speaker verification
Modern speaker verification applications require high accuracy at low complexity. We propose the use of a polynomialbased classifier to achieve this objective. We demonstrate a new combination of techniques which makes polynomial classification accurate and powerful for speaker verification. We show that discriminative training of polynomial classifiers can be performed on large data sets. A pr...
متن کاملSelective Fusion for Speaker Verification in Surveillance
This paper presents an improved speaker verification technique that is especially appropriate for surveillance scenarios. The main idea is a metalearning scheme aimed at improving fusion of lowand high-level speech information. While some existing systems fuse several classifier outputs, the proposed method uses a selective fusion scheme that takes into account conveying channel, speaking style...
متن کاملRegularized Logistic Regression Fusion for Speaker Verification
Fusion of the base classifiers is seen as the way to achieve stateof-the art performance in the speaker verfication systems. Standard approach is to pose the fusion problem as the linear binary classification task. Most successful loss function in speaker verification fusion has been the weighted logistic regression popularized by the FoCal toolkit. However, it is known that optimizing logistic...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Audio, Speech, and Language Processing
سال: 2013
ISSN: 1558-7916,1558-7924
DOI: 10.1109/tasl.2013.2256895