Shared hidden layer combination for speech recognition systems

A system comprising:

• at least one processor;
• and
• a memory operatively connected with the at least one processor, wherein the memory stores computer-executable instructions that, when executed by the at least one processor, causes the at least one processor to execute a method that comprises:
• 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 comprises 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 layer 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 system;
• 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.

A computer-readable storage device storing computer executable instructions which, when executed by a computer, cause computer to perform a method of providing a framework for merging systems having a shared deep neural network (DNN) feature transformation, the method comprising:

• receiving a plurality of training utterances for speech recognition;
• training a first system with one or more of a cross entropy criterion and a sequential training criterion utilizing the plurality of training utterances, the DNN feature transformation comprising a plurality of hidden layers;
• generating an output from a top hidden layer in the plurality of hidden layers for the plurality of training utterances;
• utilizing the top hidden layer output to generate a network comprising a low dimension bottleneck hidden layer and a plurality of senones;
• extracting one or more weights between the top hidden layer and the low dimension hidden bottleneck layer, the one or more weights representing a feature dimension reduction;
• utilizing the feature dimension reduction to train a model for a second system following the extraction of the one or more weights between the top hidden layer and the low dimension bottleneck hidden layer;
• generating a first log likelihood score from the first system based on application of the feature dimension reduction to the first system and a second log likelihood score from the second system based on application of the feature dimension to the model of the second system;
• combining the first log likelihood score and the second log likelihood score to create a merged system from the first system and the second system, wherein the first system and the second system share the DNN feature transformation;
• and
• training senone dependent combination coefficients from the merged system with the one or more of the cross entropy criterion and the sequential training criterion.