Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a deep neural network. One of the methods includes training a deep neural network with a first training set by adjusting values for each of a plurality of weights included in the neural network, and training the deep neural network to determine a probability that data received by the deep neural network has features similar to key features of one or more keywords or key phrases, the training comprising providing the deep neural network with a second training set and adjusting the values for a first subset of the plurality of weights, wherein the second training set includes data representing the key features of the one or more keywords or key phrases.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: training a deep neural network with a first training set by adjusting values for each of a plurality of weights included in the neural network; and training the deep neural network to determine a probability that data received by the deep neural network has features similar to key features of one or more keywords or key phrases, the training comprising providing the deep neural network with a second training set and adjusting the values for a first subset of the plurality of weights, wherein the second training set includes data representing the key features of the one or more keywords or key phrases. 2. The method of claim 1, wherein: the first training set has a first size; and the second training set has a second size smaller than the first size. 3. The method of claim 1, comprising: not adjusting, after training the deep neural network with the first training set, the values for a second subset of the plurality of weights while adjusting the values for the first subset of the plurality of weights, wherein the first subset of weights and the second subset of weights are disjoint subsets of the plurality of weights. 4. The method of claim 1, wherein the first training set and the second training set comprise sets of feature values for uttered speech. 5. The method of claim 4, wherein the first training set comprises sets of feature values for uttered speech from a first language and the second training set comprises sets of feature values for uttered speech from a second language different than the first language. 6. The method of claim 4, wherein the first training set comprises sets of features values for uttered speech with output values relevant to large vocabulary dictation in the same language as the one or more keywords or key phrases. 7. The method of claim 4, comprising: receiving, by the deep neural network and after providing the deep neural network with the second training set, a feature vector that models an audio waveform; and generating, by the deep neural network, a probability for each of the keywords or key phrases using the feature vector and the values of the plurality of weights. 8. The method of claim 6, comprising: generating a confidence score by combining two or more consecutive probabilities for the same keyword or key phrase, the consecutive probabilities corresponding with feature vectors that model different consecutive portions of an audio waveform; and determining whether the audio waveform included the keyword or the key phrase using the confidence score. 9. A 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: training a deep neural network with a first training set by adjusting values for each of a plurality of weights included in the neural network; and training the deep neural network to determine a probability that data received by the deep neural network has features similar to key features of one or more keywords or key phrases, the training comprising providing the deep neural network with a second training set and adjusting the values for a first subset of the plurality of weights, wherein the second training set includes data representing the key features of the one or more keywords or key phrases. 10. The system of claim 9, wherein: the first training set has a first size; and the second training set has a second size smaller than the first size. 11. The system of claim 9, the operations comprising: not adjusting, after training the deep neural network with the first training set, the values for a second subset of the plurality of weights while adjusting the values for the first subset of the plurality of weights, wherein the first subset of weights and the second subset of weights are disjoint subsets of the plurality of weights. 12. The system of claim 9, wherein the first training set and the second training set comprise sets of feature values for uttered speech. 13. The system of claim 12, wherein the first training set comprises sets of feature values for uttered speech from a first language and the second training set comprises sets of feature values for uttered speech from a second language different than the first language. 14. The system of claim 12, wherein the first training set comprises sets of features values for uttered speech with output values relevant to large vocabulary dictation in the same language as the one or more keywords or key phrases. 15. The system of claim 12, the operations comprising: receiving, by the deep neural network and after providing the deep neural network with the second training set, a feature vector that models an audio waveform; and generating, by the deep neural network, a probability for each of the keywords or key phrases using the feature vector and the values of the plurality of weights. 16. The system of claim 15, the operations comprising: generating a confidence score by combining two or more consecutive probabilities for the same keyword or key phrase, the consecutive probabilities corresponding with feature vectors that model different consecutive portions of an audio waveform; and determining whether the audio waveform included the keyword or the key phrase using the confidence score. 17. A 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: training a deep neural network with a first training set by adjusting values for each of a plurality of weights included in the neural network; and training the deep neural network to determine a probability that data received by the deep neural network has features similar to key features of one or more keywords or key phrases, the training comprising providing the deep neural network with a second training set and adjusting the values for a first subset of the plurality of weights, wherein the second training set includes data representing the key features of the one or more keywords or key phrases. 18. The computer-readable medium of claim 17, wherein: the first training set has a first size; and the second training set has a second size smaller than the first size. 19. The computer-readable medium of claim 17, the operations comprising: not adjusting, after training the deep neural network with the first training set, the values for a second subset of the plurality of weights while adjusting the values for the first subset of the plurality of weights, wherein the first subset of weights and the second subset of weights are disjoint subsets of the plurality of weights. 20. The computer-readable medium of claim 17, wherein the first training set and the second training set comprise sets of feature values for uttered speech. 21. The computer-readable medium of claim 20, wherein the first training set comprises sets of feature values for uttered speech from a first language and the second training set comprises sets of feature values for uttered speech from a second language different than the first language. 22. The computer-readable medium of claim 20, wherein the first training set comprises sets of features values for uttered speech with output values relevant to large vocabulary dictation in the same language as the one or more keywords or key phrases. 23. The computer-readable medium of claim 20, the operations comprising: receiving, by the deep neural network and after providing the deep neural network with the second training set, a feature vector that models an audio waveform; and generating, by the deep neural network, a probability for each of the keywords or key phrases using the feature vector and the values of the plurality of weights. 24. The computer-readable medium of claim 23, the operations comprising: generating a confidence score by combining two or more consecutive probabilities for the same keyword or key phrase, the consecutive probabilities corresponding with feature vectors that model different consecutive portions of an audio waveform; and determining whether the audio waveform included the keyword or the key phrase using the confidence score.