A novel estimation of feature-space MLLR for full-covariance models
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
I show how conditional Gaussians, whose means are conditioned by a random variable, can be estimated and their likelihoods computed. This is based upon how regular Gaussians have their own parameters and likelihood computed. After explaining how to estimat ...
In time series analysis state-space models provide a wide and flexible class. The basic idea is to describe an unobservable phenomenon of interest on the basis of noisy data. The first constituent of such a model is the so-called state equation, which char ...
This paper proposes a block logit (BL) model, which is an alternative approach to incorporating covariance between the random utilities of alternatives into a GEV random utility maximization model. The BL is similar to the nested logit (NL), in that it is ...
Discrete choice models are constantly in evolution in the literature. Since they enable to capture wide range of situations, they have been widely used by researchers and also practitioners in several fields of applications including econometrics and trans ...
Mixture models form the essential basis of data clustering within a statistical framework. Here, the estimation of the parameters of a mixture of Gaussian densities is considered. In this particular context, it is well known that the maximum likelihood app ...
Discrete choice models are constantly in evolution in the literature. Since they enable to capture wide range of situations, they have been widely used by researchers and also practitioners in several fields of applications including econometrics and trans ...
In this contribution, the Bayesian framework for interpretation of evidence when applied to forensic speaker recognition is introduced. Different aspects of the use of voice as evidence in the court are addressed, as well as the use by the forensic expert ...
Due to singularities of the likelihood function, the maximum likelihood approach for the estimation of the parameters of normal mixture models is an acknowledged ill posed optimization problem. Ill posedness is solved by penalizing the likelihood function. ...
This work deals with factorial models for multiple time series. Its core content puts it at the interface between statistics and finance. After a brief description of the historical link between the two sciences, it reviews the literature on factorial mode ...
In this report, we build up on our previous work on speaker clustering, where the number of speakers and segmentation boundaries are unknown a priori. We employ an ergodic HMM with minimum duration topology for this purpose. Starting from a large number of ...
