A parameter of each of a plurality of models, i.e., a first variation model, a second variation model, and an unspecified acoustic model, is estimated in such a way that an integrated degree of conformity, which is obtained by integrating a degree of conformity of the first variation model to the sample speech data, a degree of conformity of the second variation model to the sample speech data, and a degree of conformity of the unspecified acoustic model to the sample speech data, becomes the maximum. The first variation model indicates a variation, for each type of a first acoustic environment, caused in a speech by the first acoustic environment of the each type. The second variation model indicates a variation, for each type of a second acoustic environment, caused in a speech by the second acoustic environment of the each type. The unspecified acoustic model is not specified as either type of the first or second acoustic environment. As a result, when an acoustic model is built by using sample speech data affected by a plurality of acoustic environments, the influence on a speech which is caused by each of the acoustic environments can be extracted with high precision.
ACOUSTIC MODEL LEARNING DEVICE AND SPEECH RECOGNITION DEVICE