1. An acoustic model learning device comprising: a first variation model learning unit that, using sample speech data acquired through a first acoustic environment of any one type among a plurality of types, a difference in the types causing a variation in a speech, and a second acoustic environment of any one type among a plurality of types, a difference in the types causing a variation in a speech, with respect to each type of the first acoustic environment, estimates a parameter defining a first variation model indicating a variation occurring in a speech caused by the first acoustic environment of the each type; a second variation model learning unit that, using the plurality of sample speech data, with respect to each type of the second acoustic environment, estimates a parameter defining a second variation model indicating a variation occurring in a speech caused by the second acoustic environment of the each type; and an environment-independent acoustic model learning unit that, using the plurality of sample speech data, estimates a parameter defining an environment-independent acoustic model not specified as any type of the first acoustic environment and the second acoustic environment, wherein each of the learning units estimates each parameter in such a way that an integrated degree of fitness obtained by integrating a degree of fitness of the first variation model to the sample speech data, a degree of fitness of the second variation model to the sample speech data, and a degree of fitness of the environment-independent acoustic model to the sample speech data becomes the maximum.