Methods, systems, and computer programs are provided for training a front-end neural network (“front-end NN”) and a back-end neural network (“back-end NN”). The method includes: combining the back-end NN with the front-end NN so that an output layer of the front-end NN is also an input layer of the back-end NN to form a joint layer to thereby generate a combined NN; and training the combined NN for a speech recognition with a set of utterances as training data, a plurality of specific units in the joint layer being dropped during the training and the plurality of the specific units corresponding to one or more common frequency bands. The front-end NN may be configured to estimate clean frequency filter bank features from noisy input features; or, to estimate clean frequency filter bank features from noisy frequency filter bank input features in the same feature space.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for training a front-end neural network and a back-end neural network, the method comprising: combining the back-end neural network with the front-end neural network so that an output layer of the front-end neural network is also an input layer of the back-end neural network to form a joint layer to thereby generate a combined neural network; and training the combined neural network for a speech recognition with a set of utterances as training data, wherein a plurality of specific units in the joint layer are dropped during the training, the plurality of the specific units corresponding to one or more common frequency bands. 2. The method according to claim 1, wherein the front-end neural network is configured to estimate clean frequency filter bank features from noisy input features, the noisy input features having a higher-dimensional feature space than a feature space being had by the clean frequency filter bank features. 3. The method according to claim 2, wherein the clean frequency filter bank features are log Mel-frequency filter bank features. 4. The method according to claim 2, wherein the noisy input features are log-power spectral input features. 5. The method according to claim 1, wherein the front-end neural network is configured to estimate clean frequency filter bank features from noisy frequency filter bank input features in the same feature space. 6. The method according to claim 5, wherein the noisy frequency filter bank input features, the clean frequency filter bank features, or a combination of these are log Mel-frequency filter bank features. 7. The method according to claim 1, wherein the output layer of the front-end neural network is composed of a plurality of units and corresponds to a plurality of frames before and after a center frame including the center frame, and one or more common specific units in each frame are dropped as the plurality of the specific units in the joint layer. 8. The method according to claim 1, wherein the dropping is performed in an early stage of the training and then a random dropping of a plurality of random units is performed in a later stage. 9. The method according to claim 1, wherein the method further comprises: setting a plurality of specific units for the dropping, each of the specific units corresponding to a specific frequency band. 10. The method according to claim 1, the method further comprising: training the front-end neural network, wherein the trained front-end neural network is used in the combining. 11. A speech recognition system which performs a speech recognition using the combined neural network which was trained according to the method described in claim 1. 12. A system, comprising: a processor; and a memory storing a program, which, when executed on the processor, performs an operation for training a front-end neural network and a back-end neural network, the operation comprising: combining the back-end neural network for a speech recognition with the front-end neural network so that an output layer of the front-end neural network is also an input layer of the back-end neural network to form a joint layer to thereby generate a combined neural network; and training the combined neural network with a set of utterances as training data, wherein a plurality of specific units in the joint layer are dropped during the training, the plurality of the specific units corresponding to one or more common frequency bands. 13. A computer program product for training a front-end neural network and a back-end neural network, the computer program product comprising a computer usable 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: combining the back-end neural network with the front-end neural network so that an output layer of the front-end neural network is also an input layer of the back-end neural network to form a joint layer to thereby generate a combined neural network; and training the combined neural network for a speech recognition with a set of utterances as training data, wherein a plurality of specific units in the joint layer are dropped during the training, the plurality of the specific units corresponding to one or more common frequency bands. 14. The computer program product according to claim 13, wherein the front-end neural network is configured to estimate clean frequency filter bank features from noisy input features, the noisy input features having a higher-dimensional feature space than a feature space being had by the clean frequency filter bank features. 15. The computer program product according to claim 14, wherein the clean frequency filter bank features are log Mel-frequency filter bank features. 16. The computer program product according to claim 13, wherein the front-end neural network is configured to estimate clean frequency filter bank features from noisy frequency filter bank input features in the same feature space. 17. The computer program product according to claim 16, wherein the noisy input features are log-power spectral input features. 18. The computer program product according to claim 13, wherein the output layer of the front-end neural network is composed of a plurality of units and corresponds to a plurality of frames before and after a center frame including the center frame, and one or more common specific units in each frame are dropped as the plurality of the specific units in the joint layer. 19. The computer program product according to claim 13, wherein the dropping is performed in an early stage of the training and then a random dropping of a plurality of random units is performed in a later stage. 20. The computer program product according to claim 13, wherein the method further comprises: setting a plurality of specific units for the dropping, each of the specific units corresponding to a specific frequency band.