Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines its applicability to sequence learning problems, focusing on speech recognition. We apply boosting at the phoneme model level and recombine expert decisions using multi-stream techniques
In language identification and other speech applications, discriminatively trained models often outp...
In this paper, we present a new training algorithm, gradient boosting learning, for Gaussian mixture...
In the following article we discuss the practical and theoretical aspects of the SphinxTrain procedu...
Boosting is a general method for training an ensemble of classifiers with a view to improving perfor...
Boosting is a general method for training an ensemble of classifiers with a view to improving perfor...
We address the question of whether and how boosting and bagging can be used for speech recognition....
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. T...
We address the question of whether and how boosting and bagging can be used for speech recognition. ...
In this study we propose two methods to improve HMM speech recognition performance. The first method...
Boosting approaches are based on the idea that high-quality learning algorithms can be formed by rep...
The performance of automatic speech recognition (ASR) system can be significantly enhanced with addi...
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. ...
In this paper, a novel approach for speaker recognition is proposed. The system makes use of adaptiv...
We describe the use of discriminative criteria to optimize Boosting based ensembles. Boosting algori...
Artificial neural networks (ANNs) have been used to classify phonetic features in speech. The featur...
In language identification and other speech applications, discriminatively trained models often outp...
In this paper, we present a new training algorithm, gradient boosting learning, for Gaussian mixture...
In the following article we discuss the practical and theoretical aspects of the SphinxTrain procedu...
Boosting is a general method for training an ensemble of classifiers with a view to improving perfor...
Boosting is a general method for training an ensemble of classifiers with a view to improving perfor...
We address the question of whether and how boosting and bagging can be used for speech recognition....
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. T...
We address the question of whether and how boosting and bagging can be used for speech recognition. ...
In this study we propose two methods to improve HMM speech recognition performance. The first method...
Boosting approaches are based on the idea that high-quality learning algorithms can be formed by rep...
The performance of automatic speech recognition (ASR) system can be significantly enhanced with addi...
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. ...
In this paper, a novel approach for speaker recognition is proposed. The system makes use of adaptiv...
We describe the use of discriminative criteria to optimize Boosting based ensembles. Boosting algori...
Artificial neural networks (ANNs) have been used to classify phonetic features in speech. The featur...
In language identification and other speech applications, discriminatively trained models often outp...
In this paper, we present a new training algorithm, gradient boosting learning, for Gaussian mixture...
In the following article we discuss the practical and theoretical aspects of the SphinxTrain procedu...
Add to ORKG