ExpEnd is a Gauss programme for non-linear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data. The estimators included in this package are simple Poisson pseudo ML; GMM for cross section data using moment conditions based on multiplicative or additive errors; within groups fixed effects Poisson for panel data; GMM est...

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 ...

the most important objectives of monetary policy are to provide price stability, economic growth and favorable employment levels. since achieving these goals is not directly accessible for policy makers, so introducing and studying the appropriate tools and intermediate targets seem necessary. for this reason, this paper tries to answer this question: whether can we introduce an appropriate rul...

In large-scale multimedia event detection, complex target events are extracted from a large set of consumer-generated web videos taken in unconstrained environments. We devised a multimedia event detection method based on Gaussian mixture model (GMM) supervectors and support vector machines. A GMM supervector consists of the parameters of a GMM for the distribution of low-level features extract...

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underly...

This study proposes an acoustic model adaptation scheme to improve speech recognition in severely adverse environments utilizing untranscribed data. In the proposed method, a clean GMM is estimated from clean training data, and a noisecorrupted GMM is obtained by MAP adaptation over the adaptation data. The Gaussian component of the adapted HMMs is obtained using the transform of the most simil...

This paper describes a novel GMM-based bandwidth extension (BWE) method based on a sub-band basis spectrum model (SBM), in which each dimensional component represents a specific acoustic space in the frequency domain. The proposed method can achieve the BWE from a speech data with an arbitrary frequency bandwidth while the conventional methods perform the conversion from a fixed narrowband data...

We develop a GMM estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, once cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underlying distribution is unknown, we...

Speaker indexing has recently emerged as an important task due to the rapidly growing volume of audio archives. Current filtration techniques still suffer from problems both in accuracy and efficiency. In this paper an efficient method to simulate GMM scoring is presented. Simulation is done by fitting a GMM not only to every target speaker but also to every test utterance, and then computing t...