1. A method of providing a framework for merging two or more automatic speech recognition (ASR) system having a shared deep neural network (DNN) feature transformation, comprising: receiving, by a computing device, at least one utterance; training, by the computing device, the at least one utterance using a DNN feature transformation with a criterion, wherein the DNN feature transformation comprising a plurality of hidden layers; generating, by the computing device, an output from a top hidden layer in the plurality of hidden layers for the at least one utterance; utilizing, by the computing device, the top hidden later output to generate a network comprising a bottleneck layer and an output layer; extracting, by the computing device, one or more weights between the top hidden layer and the bottleneck layer, the one or more weights representing a feature dimension reduction; generating, by the computing device, a first score from a first ASR system based on application of the feature dimension reduction to a model of the first ASR system and generating a second score from a second ASR system based on application of the feature dimension reduction to a model of the second ASR; combining, by the computing device, the first score and the second score to merge the first ASR system and the second ASR system to create a merged system; and training, for the merged system, senone coefficient data for evaluation of spoken utterances.