This program will calculate the global mean and covariance of a set of training data. It is primarily used to initialise the parameters of a HMM such that all component means and all covariances are set equal to the global data mean and covariance. This might form the first stage of a flat start training scheme where all models are initially given the same parameters. Alternatively, the covariances may be used as the basis for Fixed Variance and Grand Variance training schemes. These can sometimes be beneficial in adverse conditions where a fixed covariance matrix can give increased robustness.
When training large model sets from limited data, setting a floor is often necessary to prevent variances being badly underestimated through lack of data. One way of doing this is to define a variance macro called varFloorN where N is the stream index. HCOMPV can also be used to create these variance floor macros with values equal to a specified fraction of the global variance.