Ensemble based speaker recognition using unsupervised data selection

Chien Lin Huang, Jia Ching Wang, Bin Ma

研究成果: 雜誌貢獻回顧評介論文同行評審

2 引文 斯高帕斯(Scopus)


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.

期刊APSIPA Transactions on Signal and Information Processing
出版狀態已出版 - 10 5月 2016


深入研究「Ensemble based speaker recognition using unsupervised data selection」主題。共同形成了獨特的指紋。