In a previous paper a novel Generalized Multiobjective Multitree model (GMM-model) was proposed. This model considers for the first time multitree-multicast load balancing with splitting in a multiobjective context, whose mathematical solution is a whole Pareto optimal set that can include several results than it has been possible to find in the publications surveyed. To solve the GMM-model, in...

In this paper we propose a novel speaker adaptation method for a context-dependent deep neural network HMM (CD-DNNHMM) acoustic model. The approach is based on using GMMderived features as the input to the DNN. The described technique of processing features for DNNs makes it possible to use GMM-HMM adaptation algorithms in the neural network framework. Adaptation to a new speaker can be simply ...

We explore how intrinsic variations (those associated with the speaker rather than the recording environment) affect textindependent speaker verification performance. In a previous paper we introduced the SRI-FRTIV corpus and provided speaker verification results using a Gaussian mixture model (GMM) system on telephone-channel speech. In this paper we explore the use of other speaker verificati...

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...

This paper presents a Gaussian Mixture Model (GMM) to identify the script of handwritten words of Roman, Devanagari, Kannada and Telugu scripts. It emphasizes the significance of directional energies for identification of script of the word. It is robust to varied image sizes and different styles of writing. A GMM is modeled using a set of six novel features derived from directional energy dist...

11 THE GMM ESTIMATION 2 11.1 Consistency and Asymptotic Normality . . . . . . . . . . . . . . . . . . . . . 3 11.2 Regularity Conditions and Identification . . . . . . . . . . . . . . . . . . . . . 4 11.3 The GMM Interpretation of the OLS Estimation . . . . . . . . . . . . . . . . . 5 11.4 The GMM Interpretation of the MLE . . . . . . . . . . . . . . . . . . . . . . . 6 11.5 The GMM Estimation ...

Timbral modeling is fundamental in content based music similarity systems. It is usually achieved by modeling the short term features by a Gaussian Model (GM) or Gaussian Mixture Models (GMM). In this article we propose to achieve this goal by using the GMM-supervector approach. This method allows to represent complex statistical models by an Euclidean vector. Experiments performed for the musi...

Gaussian mixture model or Gaussian mixture density model(GMM) uses the likelihood function as a measure of fit. We show that just the same algorithm as the GMM can be derived from a modified objective function of Fuzzy c-Means (FCM) clustering with the regularizer by K-L information, only when the parameter λ equals 2. Although the fixed-point iteration scheme of FCM is similar to that of the G...

I propose a nonparametric iid bootstrap that achieves asymptotic refinements for t tests and confidence intervals based on the generalized method of moments (GMM) estimators even when the model is misspecified. In addition, my bootstrap does not require recentering the bootstrap moment function, which has been considered as a critical procedure for bootstrapping GMM. The elimination of the rece...

In this paper, we propose a two-stage speech enhancement technique. In the training stage, a Gaussian Mixture Model (GMM) of the mel-frequency cepstral coefficients (MFCCs) of a user’s clean speech is computed wherein the component densities of the GMM serve to model the user’s “acoustic classes.” In the enhancement stage, MFCCs from a noisy speech signal are computed and the underlying clean a...