Methods and systems for training a neural network include identifying weights in a neural network between a final hidden neuron layer and an output neuron layer that correspond to state matches between a neuron of the final hidden neuron layer and a respective neuron of the output neuron layer. The identified weights are initialized to a predetermined non-zero value and initializing other weights between the final hidden neuron layer and the output neuron layer to zero. The neural network is trained based on a training corpus after initialization.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a neural network, comprising: identifying weights in a neural network between a final hidden neuron layer and an output neuron layer that correspond to state matches between a neuron of the final hidden neuron layer and a respective neuron of the output neuron layer; initializing the identified weights to a predetermined non-zero value and initializing other weights between the final hidden neuron layer and the output neuron layer to zero; and training the neural network based on a training corpus after initialization. 2. The method of claim 1, wherein identifying weights comprises identifying neurons on the final hidden neuron layer that have a context independent hidden Markov model state that matches a context dependent hidden Markov model state of a respective neuron of the output neuron layer. 3. The method of claim 1, wherein identifying weights comprises identifying neurons on the final hidden neuron layer that have a phone state that matches a context dependent hidden Markov model state of a respective neuron of the output neuron layer. 4. The method of claim 1, wherein identifying weights comprises identifying neurons on the final hidden neuron layer that correspond to a branch of a phonetic decision tree that includes a context dependent hidden Markov model state of a respective neuron of the output neuron layer. 5. The method of claim 1, further comprising randomly initializing weights associated with neurons in the final hidden neuron layer that do not match a state of any of the output neurons. 6. The method of claim 1, further comprising performing speech recognition using the trained neural network to convert audio voice information into textual information. 7. The method of claim 1, wherein the neural network comprises hardware weights. 8. The method of claim 1, wherein identifying weights comprises identifying multiple weights corresponding to state matches between multiple dedicated neurons in the final hidden neuron layer and multiple neurons of the output neuron layer. 9. The method of claim 8, wherein a number of the multiple weights is proportional to a number of phones in a particular branch of a phonetic decision tree. 10. A non-transitory computer readable storage medium comprising a computer readable program for training a neural network, wherein the computer readable program when executed on a computer causes the computer to perform the steps of claim 1. 11. A method for speech recognition, comprising: identifying weights in a neural network between a final hidden neuron layer and an output neuron layer that correspond to state matches between a neuron of the final hidden neuron layer and a respective neuron of the output neuron layer; initializing the identified weights to a predetermined non-zero value, randomly initializing weights associated with neurons in the final hidden neuron layer that do not match a state of any of the output neurons, and initializing all other weights between the final hidden neuron layer and the output neuron layer to zero; training the neural network based on a training corpus after initialization by updating all weights in a weight array between the final hidden neuron layer and the output neuron layer, including the identified weights; and performing speech recognition using the trained neural network to convert audio voice information into textual information. 12. A system for training a neural network, comprising: an initializing module comprising a processor configured to identify weights in a neural network between a final hidden neuron layer and an output neuron layer that correspond to state matches between a neuron of the final hidden neuron layer and a respective neuron of the output neuron layer and further configured to initialize the identified weights to a predetermined non-zero value and initializing other weights between the final hidden neuron layer and the output neuron layer to zero; and a training module configured to train the neural network based on a training corpus after initialization. 13. The system of claim 12, wherein the initializing module is further configured to identify neurons on the final hidden neuron layer that have a context independent hidden Markov model state that matches a context dependent hidden Markov model state of a respective neuron of the output neuron layer. 14. The system of claim 12, wherein initializing module is further configured to identify neurons on the final hidden neuron layer that have a phone state that matches a context dependent hidden Markov model state of a respective neuron of the output neuron layer. 15. The system of claim 12, wherein the initializing module is further configured to identify neurons on the final hidden neuron layer that correspond to a branch of a phonetic decision tree that includes a context dependent hidden Markov model state of a respective neuron of the output neuron layer. 16. The system of claim 12, wherein the initializing module is further configured to randomly initialize weights associated with neurons in the final hidden neuron layer that do not match a state of any of the output neurons. 17. The system of claim 12, further comprising a speech recognition module configured to perform speech recognition using the trained neural network to convert audio voice information into textual information. 18. The system of claim 12, wherein the neural network comprises hardware weights. 19. The system of claim 12, wherein the initializing module is further configured to identify multiple weights corresponding to state matches between multiple dedicated neurons in the final hidden neuron layer and multiple neurons of the output neuron layer. 20. The system of claim 19, wherein a number of the multiple weights is proportional to a number of phones in a respective branch of a phonetic decision tree.