In this section, we provide details on the toy example presented in Section 1. We used the following simple experimental setting. All covariances for the bidimensional distributions are diagonal, therefore all bidimensional Gaussian distributions are parametrised by 4 parameters (μ1, μ2, σ1, σ2) where μ, σ is a mean-variance pair on each dimension. We consider a data distribution that is a mixt...

This paper proposes the classification of emotions based on spectral features using the Gaussian Mixture Model as the classifier. The performance of the Gaussian Mixture Model has been evaluated for two types of databases – acted and reallife speech corpuses. The model has also been evaluated for the variation in its performance based on the speaker, gender of the speaker and the number of the ...

Speaker variability is a well-known problem of state-of-theart Automatic Speech Recognition (ASR) systems. In particular, handling children speech is challenging because of substantial differences in pronunciation of the speech units between adult and child speakers. To build accurate ASR systems for all types of speakers Hidden Markov Models with Gaussian Mixture Densities were intensively use...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy GAUSSIAN MIXTURE MODEL BASED SYSTEM IDENTIFICATION AND CONTROL By Jing Lan August 2006 Chair: José C. Principe Major Department: Electrical and Computer Engineering In this dissertation, we present a methodology of combining an improved ...

The Gaussian mixture model-universal background model (GMM-UBM) has been dominant in text-independent speaker recognition tasks. However the conventional GMM-UBM method assumes that each Gaussian mixture is independent and ignores the fact that within Gaussian mixtures, there do exist some useful high-level speaker-dependent characteristics, such as word usage or speaking habits. Based on the G...

Gaussian mixture models (GMMs) have proven extremely successful for textindependent speaker verification. The standard training method for GMM models is to use MAP adaptation of the means of the mixture components based on speech from a target speaker. In this work we look into the various models (GMM-UBM and GMM-SVM) and their application to speaker verification. In this paper, features vector...

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...

The background modeling algorithm based on Gaussian mixture model (GMM) is a widely used method in moving objects detection with static cameras. Base on the situation that traditional Gaussian mixture model is very sensitive to sudden illumination variation and is slow for convergence speed, this paper proposed a method to detect the illumination variation and update the single learning rate, i...

This study focuses on the classification of pathological voice using GMM (Gaussian Mixture Model) and compares the results to the previous work which was done by ANN (Artificial Neural Network). Speech data from normal people and patients were collected, then diagnosed and classified into two different categories. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chose...

In this paper, we proposed the principal component analysis (PCA) fuzzy mixture model for speaker identification. A PCA fuzzy mixture model is derived from the combination of the PCA and the fuzzy version of mixture model with diagonal covariance matrices. In this method, the feature vectors are first transformed by each speaker’s PCA transformation matrix to reduce the correlation among the el...