3. The computer-implemented method of claim 1 , further comprising processing the multilingual DNN acoustic model in a distributed framework comprising multiple computing devices.
4. The computer-implemented method of claim 3 , further comprising processing multiple model instances of the multilingual DNN acoustic model in parallel on subsets of training data, wherein weights determined for a respective model instance of the multiple model instances are shared through a centralized parameter server during the processing.