Ensemble classifiers using unsupervised data selection for speaker recognition

2012 ◽  
Author(s):  
Chien-Lin Huang ◽  
Chiori Hori ◽  
Hideki Kashioka ◽  
Bin Ma
Keyword(s):  
Speaker Recognition ◽  
Data Selection ◽  
Ensemble Classifiers ◽  
Selection For

scholarly journals Ensemble based speaker recognition using unsupervised data selection

2016 ◽  
Vol 5 ◽  
Author(s):  
Chien-Lin Huang ◽  
Jia-Ching Wang ◽  
Bin Ma
Keyword(s):  
Speaker Recognition ◽  
Auxiliary Information ◽  
Divide And Conquer ◽  
Data Selection ◽  
Ensemble Classifiers ◽  
Acoustic Characteristics ◽  
New Approach ◽  
Speech Data ◽  
Significant Performance ◽  
Improved Performance

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.

Data selection for naturalness in HMM-based speech synthesis

2016 ◽  
Author(s):  
Erica Cooper ◽  
Yocheved Levitan ◽  
Julia Hirschberg
Keyword(s):  
Speech Synthesis ◽  
Data Selection ◽  
Selection For

Improving data selection for low-resource STT and KWS

2015 ◽  
Author(s):  
Thiago Fraga-Silva ◽  
Antoine Laurent ◽  
Jean-Luc Gauvain ◽  
Lori Lamel ◽  
Viet-Bac Le ◽  
...  
Keyword(s):  
Data Selection ◽  
Low Resource ◽  
Selection For

Active learning based data selection for limited resource STT and KWS

2015 ◽  
Author(s):  
Thiago Fraga-Silva ◽  
Jean-Luc Gauvain ◽  
Lori Lamel ◽  
Antoine Laurent ◽  
Viet-Bac Le ◽  
...  
Keyword(s):  
Active Learning ◽  
Limited Resource ◽  
Data Selection ◽  
Selection For

Data-driven method for training data selection for deep learning

2021 ◽  
Author(s):  
C. Lacombe ◽  
I. Hammoud ◽  
J. Messud ◽  
H. Peng ◽  
T. Lesieur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Data Selection ◽  
Data Driven ◽  
Selection For ◽  
Training Data Selection
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