Sentence examples for space likelihood from inspiring English sources

Other inputs to the model include probability distribution of time to failure of mechanical ventilation in the enclosed space, likelihood of presence of an ignition source with energy ≥ minimum ignition energy (MIE) of formed flammable gas cloud, probability of leak detection prior to ignition, and conditional probabilities of fires and explosions, given ignition.

This is in spirit similar to the popular feature space maximum likelihood linear regression (fMLLR) [30] that uses the maximum likelihood criterion to estimate the linear transform.

AMONG the risks Mr. Fisher identified was a shrinking pool of prospective tenants looking to lease more than 100,000 square feet of space; the likelihood that Philadelphia will not repeat the stellar pace of 1999 and the constant exposure to mergers and acquisitions, which might open up space in otherwise fully rented buildings.

The resulting features are then transformed through a feature space maximum likelihood linear regression (FMLLR) transform [39].

Unlike the standard particle filtering method where particles are points in the state space and likelihood functions are defined by a statistical model, the box particle filter uses multidimensional intervals in the state space as particles and a bounded error model to evaluate the likelihood functions.

The end data point 〈M2, Pc2, Pn2〉 was moved in all three dimensions to generate a space of likelihood values.

In the reduced state space, the likelihood of data depends only on the combined number of selectors that are off in chains D and E combined.

Feature-space maximum likelihood linear regression (fMLLR) [7] is a common choice for reducing interspeaker variability.

Examples include the feature space maximum-likelihood linear regression (fMLLR, equivalent to global CMLLR), SPLICE feature compensation [17], etc.

More specifically, the speaker normalization using feature-space maximum likelihood linear regression (fMLLR), also known as constrained MLLR (CMLLR [54]), is applied.

This paper deals with basis feature-space maximum likelihood linear regression (basis fMLLR) [20], which can estimate transformation matrices robustly even in the cases of short utterances.