Add to ORKGCross-channel degradation is one of the significant chal-lenges facing speaker recognition systems. We study the problem for speaker recognition using support vector ma-chines (SVMs). We perform channel compensation in SVM modeling by removing non-speaker nuisance dimensions in the SVM expansion space via projections. Training to re-move these dimensions is accomplished via an eigenvalue problem. The eigenvalue problem attempts to reduce mul-tisession variation for the same speaker, reduce different channel effects, and increase “distance ” between different speakers. We apply our methods to a subset of the Switch-board 2 corpus. Experiments show dramatic improvement in performance for the cross-channel case. 1
In recent years, adaptation techniques have been given special focus in speaker recognition tasks, m...
In this paper, the sparse representation computed by l1-minimization with quadratic constraints is e...
The paper presents the test results of speaker identification system based on the Support Vector Mac...
Speaker recognition using support vector machines (SVMs) with features derived from generative model...
This paper compares two of the leading techniques for session variability compensation in the contex...
Includes bibliographical references (leaves 105-116).In this research the Support Vector Machine cla...
Recent research has demonstrated the merit of combining Gaussian mixture models and support vector m...
In this work we focus on speaker verification on channels of varying quality, namely Skype and high ...
One major source of performance decline in speaker recognition system is channel mismatch between tr...
This paper extends the within-class covariance normalization (WCCN) technique described in [1, 2] fo...
An important step in speaker verification is extracting features that best characterize the speaker ...
Abstract The use of adaptation transforms common in speech recognition systems as features for speak...
We use a multi-layer perceptron (MLP) to transform cep-stral features into features better suited fo...
Speaker recognition is the process of recognizing the speaker based on characteristics such as pitch...
Abstract—Support vector machines (SVMs), and kernel classi-fiers in general, rely on the kernel func...
In recent years, adaptation techniques have been given special focus in speaker recognition tasks, m...
In this paper, the sparse representation computed by l1-minimization with quadratic constraints is e...
The paper presents the test results of speaker identification system based on the Support Vector Mac...
Speaker recognition using support vector machines (SVMs) with features derived from generative model...
This paper compares two of the leading techniques for session variability compensation in the contex...
Includes bibliographical references (leaves 105-116).In this research the Support Vector Machine cla...
Recent research has demonstrated the merit of combining Gaussian mixture models and support vector m...
In this work we focus on speaker verification on channels of varying quality, namely Skype and high ...
One major source of performance decline in speaker recognition system is channel mismatch between tr...
This paper extends the within-class covariance normalization (WCCN) technique described in [1, 2] fo...
An important step in speaker verification is extracting features that best characterize the speaker ...
Abstract The use of adaptation transforms common in speech recognition systems as features for speak...
We use a multi-layer perceptron (MLP) to transform cep-stral features into features better suited fo...
Speaker recognition is the process of recognizing the speaker based on characteristics such as pitch...
Abstract—Support vector machines (SVMs), and kernel classi-fiers in general, rely on the kernel func...
In recent years, adaptation techniques have been given special focus in speaker recognition tasks, m...
In this paper, the sparse representation computed by l1-minimization with quadratic constraints is e...
The paper presents the test results of speaker identification system based on the Support Vector Mac...