Estimation of multiple networks in Gaussian mixture models

Penalized Likelihood Estimation in Gaussian Mixture Models

Speech quality estimation using Gaussian mixture models

We propose a novel method to estimate the quality of coded speech signals. The joint probability distribution of the subjective mean opinion score (MOS) and perceptual distortion feature variables is modelled using a Gaussian mixture density. The feature variables are sifted from a large pool of candidate features using statistical data mining techniques. We study what combinations of features ...

Direct Importance Estimation with Gaussian Mixture Models

The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extension of the Kullback-Leibler importance estimation procedure (KLIEP), an...

Speech Enhancement Using Gaussian Mixture Models, Explicit Bayesian Estimation and Wiener Filtering

Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equatio...

Discriminative mixture weight estimation for large Gaussian mixture models

This paper describes a new approach to acoustic mod-eling for large vocabulary continuous speech recognition (LVCSR) systems. Each phone is modeled with a large Gaussian mixture model (GMM) whose context-dependent mixture weights are estimated with a sentence-level discrim-inative training criterion. The estimation problem is casted in a neural network framework, which enables the incorporation...