1. A computer implemented method of corrective training for a speech recognition system that is modeled by a continuous parameter system, comprising the steps of: choosing a space of continuous parameter systems in which every point in said space gives rise to a separate continuous parameter system; transforming a textual script and a sound message that was generated by a human speaker from said textual script into electrical signal representations; producing from said electrical representations a system of constrained equations that define a domain T in said space of continuous parameter systems satisfying following: said continuous parameter system comprises a point A, that satisfies said system of constrained equations; said speech recognition system that is modeled by a continuous parameter system that corresponds to any point in said domain T decodes with not worse accuracy than said speech recognition system that is modeled by a continuous parameter system that corresponds to the point A; representing a training procedure as a constrained optimization problem of a likelihood score function defined over said domain T; and finding a new continuous parameter system in T that solves said constrained optimization problem and constructing a new speech recognition system that is modeled by said new continuous parameter system, wherein said new continuous parameter system is used to recognize sound used in said new speech recognition system for recognition of sound messages or for repeating said method of corrective training with new speech data, wherein said step of finding a new continuous parameter system in T that solves said constrained optimization problem comprises the steps: taking an iterative training step to find a point B, where B is in the space of said continuous parameter system, not necessarily contained in the constrained domain T; taking an interval I to find a new data point belonging to the constrained domain T and lying on an interval connecting point A and point B, such that any point on the interval I has a higher likelihood score than the likelihood score corresponding to A; and repeating said step of taking an interval I to find a new data point until said new data point converges to a local maximum in the constrained domain T.