1. A method comprising: obtaining access to a large reference acoustic model for automatic speech recognition, said large reference acoustic model having L states modeled by L mixture models, said large reference acoustic model having N components; identifying a desired number of components N c , less than N, to be used in a restructured acoustic model derived from said reference acoustic model, said desired number of components N c being selected based on a computing environment in which said restructured acoustic model is to be deployed, said restructured acoustic model also having L states; for each given one of said L mixture models in said reference acoustic model, building a merge sequence which records, for a given cost function, sequential mergers of pairs of said components associated with said given one of said mixture models; assigning a portion of said N c components to each of said L states in said restructured acoustic model; building said restructured acoustic model by, for each given one of said L states in said restructured acoustic model, applying said merge sequence to a corresponding one of said L mixture models in said reference acoustic model until said portion of said N c components assigned to said given one of said L states is achieved; and providing a system, wherein the system comprises distinct software modules, each of the distinct software modules being embodied on a computer-readable storage medium, and wherein the distinct software modules comprise a merge sequence generator module, a component assigner module, and a model builder module; wherein: said building of said merge sequence is carried out by said merge sequence generator module executing on at least one hardware processor; said assigning of said portion of said N c components is carried out by said component assigner module executing on said at least one hardware processor; and said building of said restructured acoustic model is carried out by said model builder module executing on said at least one hardware processor.