Conversion of a large-footprint DNN to a small-print DNN is performed using a variety of techniques, including split-vector quantization. The small-foot print DNN may be distributed to a variety of devices, including mobile devices. Further, the small-footprint DNN may aid a digital assistant on a device in interpreting speech input.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: at least one processor; and a memory storage device, the memory storage device comprising instructions, that, when executed by the at least one processor, perform a method of creating a small-footprint neural network matrix, the method comprising: analyzing a matrix of a large neural network to identify a set of sub-vectors; identifying a first codeword, wherein the first codeword approximates at least one sub-vector in the set of sub-vectors; approximating the large-neural network by replacing the at least one sub-vector with the first codeword, wherein the approximation forms a portion of a small-footprint neural network. 2. The system of claim 1, further comprising: identifying a second codeword, wherein the second codeword approximates a first plurality of sub-vectors; and identifying a third codeword, wherein the third code word approximates a second plurality of sub-vectors. 3. The system of claim 1, further comprising, after identifying the first codeword, fine-tuning the first codeword. 4. The system of claim 2, wherein the first codeword, the second codeword, and the third codeword are stored in a codebook. 5. The system of claim 1, wherein the matrix of the large neural network is shared remotely. 6. The system of claim 1, wherein the method further comprises: sending the small-footprint neural network matrix to a mobile device. 7. The system of claim 1, wherein the small-footprint neural network matrix is used to determine a likely phenome given an audio signal input. 8. A computer implemented method, the method comprising: analyzing a matrix of a large neural network to identify a set of sub-vectors; identifying a first codeword, wherein the first codeword approximates at least one sub-vector in the set of sub-vectors approximating the large-neural network by replacing the at least one sub-vector with the first codeword, wherein the approximation forms a portion of a small-footprint neural network. 9. The method of claim 8, further comprising: identifying a second codeword, wherein the second codeword approximates a first plurality of sub-vectors; and identifying a third codeword, wherein the third code word approximates a second plurality of sub-vectors. 10. The method of claim 9, further comprising, after identifying the first codeword, fine-tuning the first codeword. 11. The method of claim 9, wherein the first codeword, the second codeword, and the third codeword are stored in a codebook. 12. The method of claim 8, wherein the method further comprises: sending the small-footprint neural network matrix to a mobile device. 13. The method of claim 8, wherein the small-footprint neural network matrix is used to determine a likely phenome given an audio signal input. 14. A computer readable storage device storing instructions that perform a method when executed, the method comprising: analyzing a matrix of a large neural network to identify a set of sub-vectors; identifying a first codeword, wherein the first codeword approximates at least one sub-vector in the set of sub-vectors; approximating the large-neural network by replacing the at least one sub-vector with the first codeword, wherein the approximation forms a portion of a small-footprint neural network. 15. The computer readable storage device of claim 8, the method further comprising: identifying a second codeword, wherein the second codeword approximates a first plurality of sub-vectors; and identifying a third codeword, wherein the third code word approximates a second plurality of sub-vectors. 16. The computer readable storage device of claim 15, the method further comprising, after identifying the first codeword, fine-tuning the first codeword. 17. The computer readable storage device of claim 15, wherein the first codeword, the second codeword, and the third codeword are stored in a codebook. 18. The computer readable storage device of claim 14, wherein the method further comprises: sending the small-footprint neural network matrix to a mobile device. 19. The computer readable storage device of claim 14, wherein the small-footprint neural network matrix is used to determine a likely phenome given an audio signal input. 20. The computer readable storage device of claim 16, wherein fine tuning the code word comprises using an adaptive learning rate.