BACKGROUND

The present invention, generally, relates to domain adaptation associated with machine learning and, more particularly, to adaptation of a model for recognition processing to a target-domain.

Deep Neural Networks (DNNs), which are Artificial Neural Networks (ANNs) with multiple layers, have been widely used as models for various recognition processing systems such as Automatic Speech Recognition (ASR) systems, etc. It has been shown that the DNNs, including Feed-Forward Neural Networks (FFNNs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can achieve superior accuracy as an acoustic model for the ASR on a variety of speech recognition benchmarks.

Generally, training of the DNN from large scale data (e.g. ˜10K hours of audio data in ASR) requires huge computational cost and a considerably long time. Therefore, the DNN built from the large scale data across diverse domains may often be utilized for multiple applications instead of constructing an application-specific DNN trained for a particular target-domain from scratch.

Such DNNs can perform well for in-domain data that is covered by training data for the DNN. However, its accuracy may deteriorate for data from an out-of-domain that is different from the training-domain.

Some adaptation approaches for the DNN have been known. It has been shown that application of linear transformations to not only input features but also outputs of the internal layers yields better results in hybrid ANN/HMM (Hidden Markov Model) models (R. Gemello et al., Linear hidden transformations for adaptation of hybrid ANN/HMM models, Speech Communication, vol 49, no. 10, pp. 827-835, 2007). Also it has been demonstrated that adaptation neural network, which transforms the features and feeds them as a new input of the original speaker independent neural network, is effective (O. Abdel-Hamid et al., Rapid and Effective Speaker Adaptation of Convolutional Neural Network Based Models for Speech Recognition, Proceedings of INTERSPEECH 2013, 1248-1252, 2013.). However, aforementioned approaches both require additional computation in run-time and/or additional units/layers in the DNNs.

Also supervised adaptation can be conducted if training data with supervised information for target-domain is available. However, the supervised adaptation requires additional costs and sometimes does not work well. Also, preparing training data with supervised information such as correct labels may be practically labor intensive.

What is needed is a method, associated computer system and computer program product capable of adapting a model for recognition processing to target-domain data without involving any additional computation during recognition processing and requiring any supervised information.

SUMMARY

According to an embodiment of the present invention, there is provided a computer implemented method performed by a computer device for adapting a model for recognition processing to a target-domain. The method includes preparing a first distribution in relation to a part of the model, in which the first distribution is derived from data of a training-domain for the model. The method also includes obtaining a second distribution in relation to the part of the model by using data of the target-domain. The method further includes tuning one or more parameters of the part of the model so that difference between the first and the second distributions becomes small.

The model adapted by the method according to the embodiment of the present invention can have improved accuracy for the target-domain data without involving any additional computation during recognition processing and without requiring any supervised information.

According to a preferable embodiment of the present invention, the model includes a neural network with an input layer and a plurality of layers on top of the input layer. The part is one or more lower layers among the plurality of the layers and the input layer. The first and second distributions are output distributions from the part of the neural network by feeding the data into the input layer from the training-domain and the target-domain, respectively.

Since the part of the neural network is adjusted so that the part can transform the data of the target-domain into a space of the training-domain while keeping a remaining part of the neural network with discrimination capability, difference between the target-domain and the training-domain can be preferably absorbed at the one or more lower layers of the neural network, which enables the data of the target-domain to leverage discrimination capability provided by the neural network. Therefore, accuracy can be improved for the target-domain data without modifying network topology of the neural network.

According to other preferable embodiment of the present invention, the part includes a convolutional layer and a subsampling layer on top of the convolutional layer, and the first and second distributions are output distributions from the subsampling layer.

Therefore, even though the model includes the convolutional and subsampling layers, adaptation can be achieved appropriately since information actually discarded in the subsampling layer of the model can be preferably ignored for adaptation.

According to another preferable embodiment of the present invention, the data of the training-domain and the data of the target-domain both may be split into a plurality of classes in an unsupervised manner. The plurality of the classes includes a class representing utterance regions and a class representing silence regions. Both the first and the second distributions include a distribution for each class. The method further includes calculating difference between the first and the second distributions for each class and combines the calculated differences over the plurality of the classes.

Therefore, accuracy of the model can be further improved since the data of the silence and utterance regions can be separately taken into consideration.

According to further another preferable embodiment of the present invention, the data of the training-domain and the data of the target-domain both may be split into a plurality of classes by utilizing supervised information. The plurality of the classes includes each class representing a phone, a group of phones, or a group of multi-phones. Both the first and the second distributions include a distribution for each class. The method further includes calculating difference between the first and the second distributions for each class and combines the calculated differences over the plurality of the classes.

Therefore, accuracy of the model can be further improved since the data of each phone, each group of phones or each group of multi-phones can be separately taken into consideration.

According to further another preferable embodiment of the present invention, the method includes further performing an additional training to the tuned model by using training data with a label from the target-domain in a supervised manner.

Since the part of the model can extract a good abstracted representation for the data of the target-domain, the additional training may be worth trying. Accuracy of the model can be expected to be further improved by the additional supervised training.

Computer systems and computer program products relating to one or more aspects of the present invention are also described and claimed herein.

Further, according to other embodiment of the present invention, there is provided a computer implemented method performed by a processor for adapting a neural network to a target-domain. The method includes preparing a first output distribution from one or more lower layers of the neural network on a memory operably coupled to the processor, in which the first output distribution is derived from data of a training-domain for the neural network. The method also includes 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. The method further comprises tuning one or more parameters of the one or more lower layers of the neural network by calculating a change in the one or more parameters so as to minimize difference between the first and the second output distributions based on the first and the second output distributions stored on the memory.

The neural network adapted by the method according to other embodiment of the present invention can have improved accuracy for the target-domain data without modifying any network topology of the neural network, without involving any additional computation during run-time, and without requiring any supervised information.

Further, according to another embodiment of the present invention, there is provided a computer system for adapting a model for recognition processing to a target-domain. The computer system includes a preparing module, an obtaining module and a tuning module. The preparing module is configured to prepare a first distribution in relation to a part of the model, in which the first distribution is derived from data of a training-domain for the model. The obtaining module is configured to obtain a second distribution in relation to the part of the model by using data of the target-domain. The tuning module is configured to tune one or more parameters of the part of the model so that difference between the first and the second distributions becomes small.

Additional features and advantages are realized through the techniques of the present invention. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed invention.

