SPATIAL CLUSTERING USING THE LIKELIHOOD FUNCTION

Interval estimation using the likelihood function*

The general properties of two commonly-used methods of interval estimation for population parameters in physics are examined. Both of these methods employ the likelihood function: (i) Obtaining an interval by finding the points where the likelihood decreases from its maximum by some specified ratio; (ii) Obtaining an interval by finding points corresponding to some specified fraction of the tot...

Approximating the step change point of the process fraction non conforming using genetic algorithm to optimize the likelihood function

Control charts are standard statistical process control (SPC) tools for detecting assignable causes. These charts trigger a signal when a process gets out of control but they do not indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change. Knowing when a process has ...

Spatial Clustering Using Hierarchical SOM

Speaker clustering using direct maximisation of the MLLR-adapted likelihood

In this paper speaker clustering schemes are investigated in the context of improving unsupervised adaptation for broadcast news transcription. The various techniques are presented within a framework of top-down split-and-merge clustering. Since these schemes are to be used for MLLRbased adaptation, a natural evaluation metric for clustering is the increase in data likelihood from adaptation. T...

Speaker Clustering Using Direct Maxisation of the Mllr-adapted Likelihood

In this paper speaker clustering schemes are investigated in the context of improving unsupervised adaptation for broadcast news transcription. The various techniques are presented within a framework of top-down split-and-merge clustering. Since these schemes are to be used for MLLR-based adaptation, a natural evaluation metric for clustering is the increase in data likelihood from adaptation. ...