Adaptation of model for recognition processing

A computer system for adapting a model for recognition processing to a target-domain, by executing program instructions, the computer system comprising:

• a memory tangibly storing the program instructions;
• a processor in communications with the memory, wherein the computer system is configured to:
• prepare a first distribution in relation to a part of the model, the first distribution being derived from data of a training-domain for the model;
• obtain a second distribution in relation to the part of the model by using data of the target-domain;
• and
• tune one or more parameters of the part of the model based on the data of the target-domain and one or more output distributions from another part of the model so that a difference between the first and the second distributions becomes small by calculating a change in the one or more parameters with gradient of a loss function that represents the difference between the first and the second distributions and updating the one or more parameters based on the calculated change, the one or more parameters comprising weight matrices for a plurality of localized filters for convolution, wherein each of the target-domain and the training-domain data is split into utterance regions and silence regions and the utterance regions and the silence regions are both separately evaluated and results from each of the regions are combined for use in additional parameter tuning.

A computer program product for adapting a model for recognition processing to a target-domain, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• preparing a first distribution in relation to a part of the model, the first distribution being derived from data of a training-domain for the model;
• obtaining a second distribution in relation to the part of the model by using data of the target-domain;
• and
• tuning one or more parameters of the part of the model based on the data of the target-domain and one or more output distributions from another part of the model so that a difference between the first and the second distributions becomes small by calculating a change in the one or more parameters with gradient of a loss function that represents the difference between the first and the second distributions and updating the one or more parameters based on the calculated change, the one or more parameters comprising weight matrices for a plurality of localized filters for convolution, wherein each of the target-domain and the training-domain data is split into utterance regions and silence regions and the utterance regions and the silence regions are both separately evaluated and results from each of the regions are combined for use in additional parameter tuning.

A computer implemented method for adapting a neural network to a target-domain, performed by a processor, the method comprising:

• preparing a first output distribution from one or more lower layers of the neural network on a memory operably coupled to the processor, the first output distribution being derived from data of a training-domain for the neural network;
• calculating a second output distribution from the one or more lower layers of the neural network by feeding data of the target-domain into the neural network;
• and
• tuning one or more parameters of the one or more lower layers of the neural network based on the data of the target-domain and one or more output distributions from another part of the model by calculating a change in the one or more parameters so as to minimize a difference between the first and the second output distributions based on the first and the second output distributions stored on the memory by calculating a change in the one or more parameters with gradient of a loss function that represents the difference between the first and the second distributions and updating the one or more parameters based on the calculated change, the one or more parameters comprising weight matrices for a plurality of localized filters for convolution, wherein each of the target-domain and the training-domain data is split into utterance regions and silence regions and the utterance regions and the silence regions are both separately evaluated and results from each of the regions are combined for use in additional parameter tuning.

A computer system for adapting a model for recognition processing to a target-domain, the computer system comprising:

• a preparing module configured to prepare a first distribution in relation to a part of the model, the first distribution being derived from data of a training-domain for the model;
• an obtaining module configured to obtain a second distribution in relation to the part of the model by using data of the target-domain;
• and
• a tuning module configured to tune one or more parameters of the part of the model based on the data of the target-domain and one or more output distributions from another part of the model so that a difference between the first and the second distributions becomes small by calculating a change in the one or more parameters with gradient of a loss function that represents the difference between the first and the second distributions and updating the one or more parameters based on the calculated change, the one or more parameters comprising weight matrices for a p orality of localized filters for convolution, wherein each of the target-domain and the training-domain data is split into utterance regions and silence regions and the utterance regions and the silence regions are both separately evaluated and results from each of the regions are combined for use in additional parameter tuning.