International audienceIn this paper we study automatic regularization techniques for the fusion of automatic speaker recognition systems. Parameter regularization could dramatically reduce the fusion training time. In addition, there will not be any need for splitting the development set into different folds for cross-validation. We utilize majorization-minimization approach to automatic ridge regression learning and design a similar way to learn LASSO reg-ularization parameter automatically. By experiments we show improvement in using automatic regularization
A proven method for achieving effective automatic speech recognition (ASR) due to speaker difference...
Most automatic speech recognition (ASR) systems express probability densities over sequences of acou...
This paper describes the fusion technology for speaker recognition. First,these feature sets are ext...
International audienceIn this paper we study automatic regularization techniques for the fusion of a...
In this paper we study automatic regularization techniques for the fusion of automatic speaker recog...
Inspired by the success of least absolute shrinkage and selection operator (LASSO) in statistical le...
Fusion of the base classifiers is seen as a way to achieve high performance in state-of-the-art spea...
Recent studies in speaker recognition have shown that score-level combination of subsystems can yiel...
Regularization of linear prediction based mel-frequency cepstral coefficient (MFCC) extraction in sp...
Abstract—Regularization of linear prediction based mel-fre-quency cepstral coefficient (MFCC) extrac...
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting c...
An important research direction in speech technology is robust cross-corpus and cross-language emoti...
In this paper, a novel speaker normalization method is presented and compared to a well known vocal ...
This paper introduces a method for regularization of HMM sys-tems that avoids parameter overfitting ...
A new front-end normalization algorithm that uses a parametric non-linear transformation is proposed...
A proven method for achieving effective automatic speech recognition (ASR) due to speaker difference...
Most automatic speech recognition (ASR) systems express probability densities over sequences of acou...
This paper describes the fusion technology for speaker recognition. First,these feature sets are ext...
International audienceIn this paper we study automatic regularization techniques for the fusion of a...
In this paper we study automatic regularization techniques for the fusion of automatic speaker recog...
Inspired by the success of least absolute shrinkage and selection operator (LASSO) in statistical le...
Fusion of the base classifiers is seen as a way to achieve high performance in state-of-the-art spea...
Recent studies in speaker recognition have shown that score-level combination of subsystems can yiel...
Regularization of linear prediction based mel-frequency cepstral coefficient (MFCC) extraction in sp...
Abstract—Regularization of linear prediction based mel-fre-quency cepstral coefficient (MFCC) extrac...
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting c...
An important research direction in speech technology is robust cross-corpus and cross-language emoti...
In this paper, a novel speaker normalization method is presented and compared to a well known vocal ...
This paper introduces a method for regularization of HMM sys-tems that avoids parameter overfitting ...
A new front-end normalization algorithm that uses a parametric non-linear transformation is proposed...
A proven method for achieving effective automatic speech recognition (ASR) due to speaker difference...
Most automatic speech recognition (ASR) systems express probability densities over sequences of acou...
This paper describes the fusion technology for speaker recognition. First,these feature sets are ext...
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