1. A computer-implemented method for speech recognition, the computer-implemented method comprising: accessing acoustic data of a first speaker, the acoustic data of the first speaker being a collection of recorded utterances spoken by the first speaker; accessing a baseline acoustic speech model of an automated speech recognition system, the baseline acoustic speech model having a plurality of acoustic parameters used in converting spoken words to text; estimating, using a maximum a posteriori probability process, statistical changes to acoustic parameters of the baseline acoustic speech model that improve speech recognition accuracy of the acoustic model when executing speech recognition on utterances of the first speaker, wherein using the maximum a posteriori probability process includes comparing an analysis of the acoustic data of the first speaker to the plurality of acoustic parameters of the baseline acoustic speech model, wherein using the maximum a posteriori probability process includes restricting estimation of statistical changes such that an amount of acoustic parameters from the baseline acoustic speech model that have an estimated statistical change is less than a total number of acoustic parameters included in the baseline acoustic speech model; and storing changes to a set of acoustic parameters corresponding to acoustic parameters from the baseline acoustic speech model that have an estimated statistical change, the changes being stored as acoustic parameter adaptation data linked to the first speaker.
3. The computer-implemented method of claim 1 , wherein restricting estimation of statistical changes includes imposing a penalty on movement of exponential family variables when calculating statistical change.