Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a deep neural network. One of the methods for training a deep neural network that includes a low rank hidden input layer and an adjoining hidden layer, the low rank hidden input layer including a first matrix A and a second matrix B with dimensions i×m and m×o, respectively, to identify a keyword includes receiving a feature vector including i values that represent features of an audio signal encoding an utterance, determining, using the low rank hidden input layer, an output vector including o values using the feature vector, determining, using the adjoining hidden layer, another vector using the output vector, determining a confidence score that indicates whether the utterance includes the keyword using the other vector, and adjusting weights for the low rank hidden input layer using the confidence score.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a deep neural network that comprises a low rank hidden input layer and an adjoining hidden layer, the low rank hidden input layer comprising a first matrix A and a second matrix B with dimensions i×m and m×o, respectively, to identify a keyword, the method comprising: receiving a feature vector comprising i values that represent features of an audio signal encoding an utterance; determining, using the low rank hidden input layer, an output vector comprising o values using the feature vector; determining, using the adjoining hidden layer, another vector using the output vector; determining a confidence score that indicates whether the utterance includes the keyword using the other vector; and adjusting one or more weights for the low rank hidden input layer based on an accuracy of the confidence score. 2. The method of claim 1 wherein adjusting the one or more weights for the low rank hidden input layer based on the accuracy of the confidence score comprises adjusting a greater quantity of the weights in the low rank hidden input layer when the accuracy does not satisfy a threshold accuracy than a smaller quantity of the weights that would be adjusted when the accuracy satisfies the threshold accuracy. 3. The method of claim 1 comprising: determining a posterior probability score using the other vector, wherein determining the confidence score using the other vector comprises determining the confidence score using the posterior probability score. 4. The method of claim 1 comprising: providing the deep neural network to a digital signal processor for processing audio signals. 5. The method of claim 1 comprising: combining, by the low rank hidden input layer, the values of the first matrix A with the second matrix B using a linear function. 6. The method of claim 5 wherein the linear function comprises a weighted sum. 7. The method of claim 1 wherein determining, using the adjoining hidden layer, the other vector using the output vector comprises determining the other vector using a non-linear function. 8. The method of claim 7 wherein the non-linear function comprises a rectified linear unit (ReLU) function. 9. A system for training a deep neural network that comprises a low rank hidden input layer and an adjoining hidden layer, the low rank hidden input layer comprising a first matrix A and a second matrix B with dimensions i×m and m×o, respectively, to identify a keyword, the system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a feature vector comprising i values that represent features of an audio signal encoding an utterance; determining, using the low rank hidden input layer, an output vector comprising o values using the feature vector; determining, using the adjoining hidden layer, another vector using the output vector; determining a confidence score that indicates whether the utterance includes the keyword using the other vector; and adjusting one or more weights for the low rank hidden input layer based on an accuracy of the confidence score. 10. The system of claim 9 wherein adjusting the one or more weights for the low rank hidden input layer based on the accuracy of the confidence score comprises adjusting a greater quantity of the weights in the low rank hidden input layer when the accuracy does not satisfy a threshold accuracy than a smaller quantity of the weights that would be adjusted when the accuracy satisfies the threshold accuracy. 11. The system of claim 9 the operations comprising: determining a posterior probability score using the other vector, wherein determining the confidence score using the other vector comprises determining the confidence score using the posterior probability score. 12. The system of claim 9 the operations comprising: providing the deep neural network to a digital signal processor for processing audio signals. 13. The system of claim 9 the operations comprising: combining, by the low rank hidden input layer, the values of the first matrix A with the second matrix B using a linear function. 14. The system of claim 13 wherein the linear function comprises a weighted sum. 15. The system of claim 9 wherein determining, using the adjoining hidden layer, the other vector using the output vector comprises determining the other vector using a non-linear function. 16. The system of claim 15 wherein the non-linear function comprises a rectified linear unit (ReLU) function. 17. A computer-readable medium for training a deep neural network that comprises a low rank hidden input layer and an adjoining hidden layer, the low rank hidden input layer comprising a first matrix A and a second matrix B with dimensions i×m and m×o, respectively, to identify a keyword, the computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: receiving a feature vector comprising i values that represent features of an audio signal encoding an utterance; determining, using the low rank hidden input layer, an output vector comprising o values using the feature vector; determining, using the adjoining hidden layer, another vector using the output vector; determining a confidence score that indicates whether the utterance includes the keyword using the other vector; and adjusting one or more weights for the low rank hidden input layer based on an accuracy of the confidence score. 18. The computer-readable medium of claim 17 wherein adjusting the one or more weights for the low rank hidden input layer based on the accuracy of the confidence score comprises adjusting a greater quantity of the weights in the low rank hidden input layer when the accuracy does not satisfy a threshold accuracy than a smaller quantity of the weights that would be adjusted when the accuracy satisfies the threshold accuracy. 19. The computer-readable medium of claim 17 the operations comprising: providing the deep neural network to a digital signal processor for processing audio signals. 20. The computer-readable medium of claim 17 the operations comprising: combining, by the low rank hidden input layer, the values of the first matrix A with the second matrix B using a linear function.