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 examines the impacts of real exchange rate fluctuations on the companies' strategic investments in Iran. The data of 92 listed companies in Tehran Stock Exchange during the period of 2002-2015areused. First, the volatility of exchange rate is estimated by the Generalized Autoregressive Conditional Heteroskedasticity (GARCH). The model is estimated by GMM and system GMM methods. The r...

Subspace Gaussian mixture model(GMM) is an alternative approach to approximate the probabilistic density function (p.d.f) of a set of independent identical distributed (i.i.d) data with prior density estimates. In this approach, the prior density of GMM parameters is estimated from a development dataset, and when predict the new enrolled data, the prior knowledge can be utilised by criteria lik...

A technique for refining the anchor modelling system introduced enhanced emotion detection system from speech. Anchor representation was then put on the speaker detection issue. Identification mistake transaction productivity uncovered that the anchor displaying system missed the mark supply of a state-of the-craftsmanship GMM-UBM framework. It had been more observed that its computational effi...

The Gaussian mixture models (GMM) has proved to be an effective probabilistic model for speaker verification, and has been widely used in most of state-of-the-art systems. In this paper, we introduce a new method for the task: that using AdaBoost learning based on the GMM. The motivation is the following: While a GMM linearly combines a number of Gaussian models according to a set of mixing wei...

Properties of GMM estimators for panel data, which have become very popular in the empirical economic growth literature, are not well known when the number of individuals is small. This paper analyses through Monte Carlo simulations the properties of various GMM and other estimators when the number of individuals is the one typically available in country growth studies. It is found that, provid...

The three ICSI systems involved in the evaluations are the keyword HMM supervector system [1], the GMM supervector system, and the keyword phone lattice N-grams system [2], which we enhanced by including prosodic N-grams. Descriptions of the keyword HMM supervector and keyword phone lattice Ngrams + prosodic N-grams systems will be discussed in sections 3 and 4. A description of the GMM superve...

This paper compares the performance of three recently proposed estimators for dynamic panel data models (LSDV bias-corrected, MLE and MDE) along with GMM. Using Monte-Carlo, we find that MLE and biascorrected estimators have the smallest bias and are good alternatives for the GMM. System-GMM outperforms the rest in ‘difficult’ designs. Unfortunately, bias-corrected estimator is not reliable in ...

The article describes a speaker recognition system based on continuous speech using GMM multivariate probability distributions. A theoretical model of the system including the extraction of distinctive features and statistical modeling is described. The efficiency of the system implemented in the Linux operating system was determined. The system is designed to support the functionality of the P...

In this paper, methods of Gaussian Mixture Model (GMM) are presented for both silence/voiced/voiceless segmentation and tone decision in Mandarin continuous speech recognition system. GMM has been used for silence/voiced/voiceless segmentation before, but the feature parameters can be modified to improve both accuracy and speed. As a popular method in pattern recognition, GMM is first proposed ...