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

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

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

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.

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

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

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

(GMMs) calle problems in o to increase th densities follo healthy infant mature infants sional Mel-Fr method for tra based re-estim

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

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