Ensemble based speaker recognition using unsupervised data selection

APSIPA Transactions on Signal and Information Processing

Bin Ma¹  Chien-Lin Huang²  Jia-Ching Wang² 

[1]Institute for Infocomm Research (I2R)

[2]National Central University

关键词: Speaker recognition;    Ensemble classifier;    Unsupervised data selection;   

DOI  :  10.1017/ATSIP.2016.10

学科分类：计算机科学（综合）

来源: Cambridge University Press

【 摘 要 】

This paper presents an ensemble-based speaker recognition using unsupervised data selection. Ensemble learning is a type of machine learning that applies a combination of several weak learners to achieve an improved performance than a single learner. A speech utterance is divided into several subsets based on its acoustic characteristics using unsupervised data selection methods. The ensemble classifiers are then trained with these non-overlapping subsets of speech data to improve the recognition accuracy. This new approach has two advantages. First, without any auxiliary information, we use ensemble classifiers based on unsupervised data selection to make use of different acoustic characteristics of speech data. Second, in ensemble classifiers, we apply the divide-and-conquer strategy to avoid a local optimization in the training of a single classifier. Our experiments on the 2010 and 2008 NIST Speaker Recognition Evaluation datasets show that using ensemble classifiers yields a significant performance gain.

【 授权许可】

Unknown