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67
Speaker verification using Adapted Gaussian mixture models
- Digital Signal Processing
, 2000
"... In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but ef ..."
Abstract
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Cited by 1010 (42 self)
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but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance
THE APPLICATION OF BAYES YING-YANG HARMONY BASED GMMS IN ON-LINE SIGNATURE VERIFICATION
"... ABSTRACT In this contribution, a Bayes Ying-Yang(BYY) harmony based approach for on-line signature verification is presented. In the proposed method, a simple but effective Gaussian Mixture Models(GMMs) is used to represent for each user's signature model based on the prior information collect ..."
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ABSTRACT In this contribution, a Bayes Ying-Yang(BYY) harmony based approach for on-line signature verification is presented. In the proposed method, a simple but effective Gaussian Mixture Models(GMMs) is used to represent for each user's signature model based on the prior information
HYBRID TEXT-INDEPENDENT SPEAKER RECOGNITION USING CHARACTER-BASED BACKGROUND HMMS AND GMMS FOR MANDARIN SPEECH
"... In mandarin, the words are composed by the concatenation of Chinese characters. In this paper, we propose a hybrid speaker recognition system based on character-based background HMMs and Gaussian mixture models to combine the advantage of them for text-independent Mandarin speech. Here all character ..."
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the segments only containing silence and noise from utterances, then the speech segments are used to train the GMMs for text-independent speaker recognition and to specify scoring segments for test utterances. Furthermore, it provides speaker-independent background likelihood scores for verification
Fuzzy Gaussian Mixture Models for Speaker Recognition
- In a special issue of the Australian Journal of Intelligent Information Processing Systems (AJIIPS
, 1999
"... A fuzzy clustering based modification of Gaussian mixture models (GMMs) for speaker recognition is proposed. In this modification, fuzzy mixture weights are introduced by redefining the distances used in the fuzzy c-means (FCM) functionals. Their reestimation formulas are proved by minimising the FC ..."
Abstract
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Cited by 13 (6 self)
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the FCM functionals. The experimental results show that the fuzzy GMMs can be used in speaker recognition and it is more effective than the GMMs in tests on the TI46 database. 1.
Effective And Efficient Sports Highlights Extraction Using The
- Proc. of ICME
, 2004
"... In fitting the training data with Guassian Mixture Models (GMMs) of appropriate structures using the MDL criterion, we are able to improve audio classification accuracy with a large margin. With the MDL-GMMs, we are also able to greatly improve the accuracy in extracting sports highlights. Since we ..."
Abstract
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Cited by 3 (2 self)
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In fitting the training data with Guassian Mixture Models (GMMs) of appropriate structures using the MDL criterion, we are able to improve audio classification accuracy with a large margin. With the MDL-GMMs, we are also able to greatly improve the accuracy in extracting sports highlights. Since we
Confidence-based policy learning from demonstration using gaussian mixture models
- in Joint Conference on Autonomous Agents and Multi-Agent Systems
, 2007
"... We contribute an approach for interactive policy learning through expert demonstration that allows an agent to actively request and effectively represent demonstration examples. In order to address the inherent uncertainty of human demonstration, we represent the policy as a set of Gaussian mixture ..."
Abstract
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Cited by 57 (5 self)
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We contribute an approach for interactive policy learning through expert demonstration that allows an agent to actively request and effectively represent demonstration examples. In order to address the inherent uncertainty of human demonstration, we represent the policy as a set of Gaussian mixture
EMOTION RECOGNITION FROM SPEECH VIA BOOSTED GAUSSIAN MIXTURE MODELS
"... Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic features and their parameters are estimated by the ..."
Abstract
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Cited by 5 (1 self)
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Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic features and their parameters are estimated
Deep convolutional neural networks for LVCSR,” ICASSP
, 2013
"... Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, CNNs are a more effective model for speech compared to Deep Neu ..."
Abstract
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Cited by 28 (2 self)
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Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, CNNs are a more effective model for speech compared to Deep
Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification: A Comparative Study
- A COMPARATIVE STUDY. NEURAL COMPUTING AND APPLICATIONS
, 1999
"... Probabilistic decision-based neural networks (PDBNNs) can be considered as a special form of Gaussian mixture models (GMMs) with trainable decision thresholds. This paper is to provide detailed illustrations to compare the recognition accuracy and decision boundaries of PDBNNs with that of GMMs thro ..."
Abstract
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Cited by 5 (1 self)
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Probabilistic decision-based neural networks (PDBNNs) can be considered as a special form of Gaussian mixture models (GMMs) with trainable decision thresholds. This paper is to provide detailed illustrations to compare the recognition accuracy and decision boundaries of PDBNNs with that of GMMs
A novel earth mover’s distance methodology for image matching with gaussian mixture models
- In ICCV, 2013
"... The similarity or distance measure between Gaussian mixture models (GMMs) plays a crucial role in contentbased image matching. Though the Earth Mover’s Distance (EMD) has shown its advantages in matching histogram features, its potentials in matching GMMs remain unclear and are not fully explored. T ..."
Abstract
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Cited by 4 (0 self)
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the first attempt to learn the EMD distance metrics between GMMs by using a simple yet effective supervised pair-wise based method. It can adapt the distance metrics between GMMs to specific classification tasks. The proposed method is evaluated on both simulated data and benchmark real databases
Results 1 - 10
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67
