A method for transferring acoustic knowledge of a trained acoustic model (AM) to a neural network (NN) includes reading, into memory, the NN and the AM, the AM being trained with target domain data, and a set of training data including a set of phoneme data, the set of training data being data obtained from a domain different from a target domain for the target domain data, inputting training data from the set of training data into the AM, calculating one or more posterior probabilities of context-dependent states corresponding to phonemes in a phoneme class of a phoneme to which each frame in the training data belongs, and generating a posterior probability vector from the one or more posterior probabilities, as a soft label for the NN, and inputting the training data into the NN and updating the NN, using the soft label.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for transferring acoustic knowledge of a trained acoustic model (AM) to a neural network (NN), the method comprising: reading, into memory, the NN and the AM, the AM being trained with target domain data, and a set of training data including a set of phoneme data, the set of training data being data obtained from a domain different from a target domain for the target domain data; inputting training data from the set of training data into the AM, calculating one or more posterior probabilities of context-dependent states corresponding to phonemes in a phoneme class of a phoneme to which each frame in the training data belongs, and generating a posterior probability vector from the one or more posterior probabilities, as a soft label for the NN; and inputting the training data into the NN and updating the NN, using the soft label. 2. The method according to claim 1, wherein the phoneme class to which each frame in the training data belongs is retrieved from a set of phoneme classes in the memory and an attribute of each class is defined based on at least acoustic characteristics of phonemes. 3. The method according to claim 1, the method further comprising, in response to calculating the one or more posterior probabilities, replacing at least one posterior probability which is less than a predetermined threshold with a constant value. 4. The method according to claim 1, wherein the phonemes in the phoneme class have a same type of several distinctive features. 5. The method according to claim 1, wherein the AM is a Gaussian Mixture Model (GMM)-based AM or a NN-based AM. 6. The method according to claim 1, further comprising using the updated neural network in a speech recognition system to perform speech recognition. 7. A system comprising: a processor; and a memory storing a program, which, when executed on the processor, performs a method for transferring acoustic knowledge of a trained acoustic model (AM) to a neural network (NN), the method comprising: reading, into the memory, the NN and the AM, the AM being trained with target domain data, and training data including a set of phoneme data, the set of training data being data obtained from a domain different from a target domain for the target domain data; inputting training data from the set of training data into the AM, calculating one or more posterior probabilities of context-dependent states corresponding to phonemes in a phoneme class of a phoneme to which each frame in the training data belongs, and generating a posterior probability vector from the one or more posterior probabilities, as a soft label for the NN; and inputting the training data into the NN and updating the NN, using the soft label. 8. The system according to claim 7, wherein the phoneme class to which each frame in the training data belongs is retrieved from a set of phoneme classes in the memory and an attribute of each class is defined based on at least acoustic characteristics of phonemes. 9. The system according to claim 7, wherein the method further comprises, in response to calculating the one or more posterior probabilities, replacing at least one posterior probability which is less than a predetermined threshold with a constant value. 10. The system according to claim 7, wherein the phonemes in the phoneme class have a same type of distinctive features. 11. The system according to claim 7, wherein the AM is a Gaussian Mixture Model (GMM)-based AM or a NN-based AM. 12. A non-transitory computer program product for transferring acoustic knowledge of a trained acoustic model (AM) to a neural network (NN), the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a computer to cause the computer to perform a method comprising: reading, into memory, the NN and the AM, the AM being trained with target domain data, and a set of training data including a set of phoneme data, the set of training data being data obtained from a domain different from a target domain for the target domain data; inputting training data from the set of training data into the AM, calculating one or more posterior probabilities of context-dependent states corresponding to phonemes in a phoneme class of a phoneme to which each frame in the training data belongs, and generating a posterior probability vector from the one or more posterior probabilities, as a soft label for the NN; and inputting the training data into the NN and updating the NN, using the soft label. 13. The computer program product according to claim 12, wherein the phoneme class to which each frame in the training data belongs is retrieved from a set of phoneme classes in the memory and an attribute of each class is defined based on at least acoustic characteristics of phonemes. 14. The computer program product according to claim 12, wherein the method further comprises, in response to calculating the one or more posterior probabilities, replacing at least one posterior probability which is less than a predetermined threshold with a constant value. 15. The computer program product according to claim 12, wherein the phonemes in the phoneme class have a same type of distinctive features. 16. The computer program product according to claim 12, wherein the AM is a Gaussian Mixture Model (GMM)-based AM or a NN-based AM.