The paper considers text independent speaker identification over the telephone using short training and testing data. Gaussian Mixture Modeling (GMM) is used in the testing phase, but the parameters of the model are taken from clusters obtained for the training data by an adequate choice of feature vectors and a distance measure without optimization in the maximum likelihood (ML) sense. This di...

The performance of K-means and Gaussian mixture model (GMM) clustering depends on the initial guess of partitions. Typically, clus∗corresponding author

Fast speaker verification systems can be realised by reducing the computation associated with searching of mixture components within the statistical model such as a Gaussian mixture model, GMM. Several improvements regarding computational efficiency have already been proposed for speaker verification. In this paper, the technique of Gaussian selection is applied to the speaker verification task...

Gaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components in speech decoding. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performance ...

In earlier studies, we employed a large prosodic feature vector to assess the quality of L2 learner’s utterances with respect to sentence melody and rhythm. In this paper, we combine these features with two standard approaches in paralinguistic analysis: (1) features derived from a Gaussian Mixture Model used as Universal Background Model (GMM-UBM), and (2) openSMILE, an open-source toolkit for...

A spectral conversion method using multiple Gaussian Mixture Models (GMMs) based on the Bayesian framework is proposed. A typical spectral conversion framework is based on a GMM. However, in this conventional method, a GMM-appropriate number of mixtures is dependent on the amount of training data, and thus the number of mixtures should be determined beforehand. In the proposed method, the varia...

Gaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components of speech recognition. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performan...

Methods for missing-feature reconstruction substitute noisecorrupted features with clean-speech estimates calculated based on reliable information found in the noisy speech signal. Gaussian mixture model (GMM) based reconstruction has conventionally focussed on reliable information present in a single frame. In this work, GMM-based reconstruction is applied on windows that span several time fra...

This paper is concerned with compressive sensing of signals drawn from a Gaussian mixture model (GMM) with sparse precision matrices. Previous work has shown: (i) a signal drawn from a given GMM can be perfectly reconstructed from r noise-free measurements if the (dominant) rank of each covariance matrix is less than r; (ii) a sparse Gaussian graphical model can be efficiently estimated from fu...

speaker verification is the process of accepting or rejecting claimed identity in terms of its sound features. a speaker verification system can be used for numerous security systems, including bank account accessing, getting to security points, criminology and etc. when a speaker verification system wants to check the identity of individuals remotely, it confronts problems such as noise effect...