Training a device specific acoustic model

A non-transitory computer readable medium storing code that, if executed by one or more computers, would cause the one or more computers to:

• store a plurality of acoustic models associated with a device;
• receive metadata including information according to which one or more trained acoustic models, of the plurality of acoustic models that are already trained, is selected;
• preselect, based on the metadata, one or more trained acoustic models of the plurality of acoustic models;
• receive speech audio including natural language utterances;
• select a trained acoustic model from the preselected one or more trained acoustic models, the selected trained acoustic model being trained with environmental features;
• and
• employ the selected trained acoustic model to recognize speech from the natural language utterances included in the received speech audio.

A non-transitory computer readable medium storing code that, if executed by one or more computers, would cause the one or more computers to:

• detect information useful for selecting an acoustic model from a plurality of acoustic models that are already trained and indicative of a device condition;
• receive speech audio;
• transmit the detected information and the received speech audio;
• and
• receive information requested by speech in the speech audio,
• wherein the detected information is capable of being employed to preselect and select a trained acoustic model from a plurality of acoustic models associated with different device conditions, the trained acoustic model being trained with environmental features, and the detected information being capable of being used to recognize speech from the received speech audio.