Techniques related to implementing neural networks for speech recognition systems are discussed. Such techniques may include processing a node of the neural network by determining a score for the node as a product of weights and inputs such that the weights are fixed point integer values, applying a correction to the score based a correction value associated with at least one of the weights, and generating an output from the node based on the corrected score.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-25. (canceled) 26. A computer-implemented method for processing a node of a neural network comprising: determining a score for the node of the neural network, wherein the score comprises a sum of products of inputs to the node and weights associated with the node, and wherein the weights comprise fixed point integer values; modifying the score based on a bias associated with the node; applying at least one correction to the score based on at least one weight of the weights having an associated correction value; and generating an output from the node based on the corrected biased score. 27. The method of claim 26, wherein the weights comprise 8 bit signed fixed point integer values having an associated scaling factor. 28. The method of claim 27, wherein the neural network comprises a neural network layer including the node, the method further comprising: evaluating a corrections list associated with the neural network layer to determine the correction value, wherein the corrections list comprises correction values for one or more neural network layer weights of the neural network layer. 29. The method of claim 28, further comprising: determining the scaling factor for the neural network layer weights of the neural network layer as a maximum scaling factor value that provides a corrections count for the neural network layer that is less than a predetermined corrections limit, wherein the neural network layer weights comprise the weights associated with the node. 30. The method of claim 28, further comprising: converting floating point value neural network layer weights to the neural layer network layer weights, wherein the neural network layer weights comprise 8 bit signed fixed point integer values; and generating the corrections list comprising the correction values for the one or more neural network layer weights and associated node and weight indices for the correction values, wherein the correction values are associated with 8 bit unsigned fixed point integer neural network layer weights having a non-zero most significant bit. 31. The method of claim 26, wherein the node comprises a hidden layer node and wherein generating the output from the node based on the corrected biased score comprises applying an activation function to the corrected biased score to generate the output. 32. The method of claim 31, wherein the activation function comprises a sigmoid function and wherein the output comprises an 8 bit unsigned fixed point integer value. 33. The method of claim 26, wherein the node comprises an output node and wherein generating the output from the node based on the corrected biased score comprises providing the corrected biased score as the output. 34. The method of claim 26, wherein the corrected biased score comprises a 32 bit fixed point integer value. 35. The method of claim 26, wherein modifying the score by the bias is performed prior to applying the at least one correction to the score. 36. The method of claim 26, wherein the neural network comprises a speech recognition neural network, the method further comprising: converting received speech to a speech recording; extracting feature vectors associated with time windows of the speech recording; providing the feature vectors as input to the neural network; generating classification scores from the speech recognition neural network based at least in part on the output from the node; and determining a sequence of textual elements based on the classification scores. 37. The method of claim 36, wherein the weights comprise 8 bit signed fixed point integer values having an associated scaling factor, wherein the inputs comprises 8 bit unsigned fixed point integer values, wherein the corrected biased score comprises a 32 bit fixed point integer value, wherein the node comprises a hidden layer node, wherein generating the output from the node based on the corrected biased score comprises applying a sigmoid activation function to the corrected biased score to generate the output, and wherein the output comprises an 8 bit unsigned fixed point integer value. 38. The method of claim 36, wherein the input to the neural network comprises a stack of feature vectors and wherein determining the sequence of textual elements comprises comparing the classification scores to statistical models. 39. A system for providing a neural network comprising: a memory configured to store weights associated with a node of the neural network, wherein the weights comprise fixed point integer values; and a central processing unit coupled to the memory, wherein the central processing unit comprises: node scoring circuitry configured to determine a score for the node of the neural network, wherein the score comprises a sum of products of inputs to the node and the weights associated with the node; score bias circuitry configured to modify the score based on a bias associated with the node; score correction circuitry configured to apply at least one correction to the score based on at least one weight of the weights having an associated correction value; and activation function circuitry configured to apply an activation function to the corrected biased score to generate an output from the node. 40. The system of claim 39, wherein the weights comprise 8 bit signed fixed point integer values having an associated scaling factor, wherein the neural network comprises a neural network layer including the node, and wherein the score correction circuitry is further configured to: evaluate a corrections list associated with the neural network layer to determine the correction value, wherein the corrections list comprises correction values for one or more neural network layer weights of the neural network layer. 41. The system of claim 39, wherein the activation function comprises a sigmoid function and wherein the output comprises an 8 bit unsigned fixed point integer value. 42. The system of claim 39, wherein the corrected biased score comprises a 32 bit fixed point integer value. 43. The system of claim 39, wherein the weights comprise 8 bit signed fixed point integer values having an associated scaling factor, wherein the inputs comprises 8 bit unsigned fixed point integer values, wherein the corrected biased score comprises a 32 bit fixed point integer value, wherein the node comprises a hidden layer node, wherein the activation function comprises a sigmoid activation function, and wherein the output comprises an 8 bit unsigned fixed point integer value. 44. The system of claim 39 further comprising: feature extraction circuitry configured to extract feature vectors associated with time windows of a speech recording and to provide the feature vectors as input to the neural network; and textual elements search circuitry configured to receive classification scores from the neural network, wherein the classification scores are based at least in part on the output from the node, and to determine a sequence of textual elements based on the classification scores. 45. At least one machine readable medium comprising a plurality of instructions that, in response to being executed on a computing device, cause the computing device to process a node of a neural network by: determining a score for the node of the neural network, wherein the score comprises a sum of products of inputs to the node and weights associated with the node, and wherein the weights comprise fixed point integer values; modifying the score based on a bias associated with the node; applying at least one correction to the score based on at least one weight of the weights having an associated correction value; and generating an output from the node based on the corrected biased score. 46. The machine readable medium of claim 45, wherein the weights comprise 8 bit signed fixed point integer values having an associated scaling factor, wherein the neural network comprises a neural network layer including the node, the machine readable medium further comprising instructions that, in response to being executed on the computing device, cause the computing device to process a node of a neural network by: evaluating a corrections list associated with the neural network layer to determine the correction value, wherein the corrections list comprises correction values for one or more neural network layer weights of the neural network layer. 47. The machine readable medium of claim 45, wherein the node comprises a hidden layer node, wherein generating the output from the node based on the corrected biased score comprises applying a sigmoid activation function to the corrected biased score to generate the output, wherein the output comprises an 8 bit unsigned fixed point integer value. 48. The machine readable medium of claim 45, wherein the corrected biased score comprises a 32 bit fixed point integer value. 49. The machine readable medium of claim 45, wherein the neural network comprises a speech recognition neural network, the machine readable medium further comprising instructions that, in response to being executed on the computing device, cause the computing device to perform speech recognition by: converting received speech to a speech recording; extracting feature vectors associated with time windows of the speech recording; providing the feature vectors as input to the neural network; generating classification scores from the speech recognition neural network based at least in part on the output from the node; and determining a sequence of textual elements based on the classification scores. 50. The machine readable medium of claim 49, wherein the weights comprise 8 bit signed fixed point integer values having an associated scaling factor, wherein the inputs comprises 8 bit unsigned fixed point integer values, wherein the corrected biased score comprises a 32 bit fixed point integer value, wherein the node comprises a hidden layer node, wherein generating the output from the node based on the corrected biased score comprises applying a sigmoid activation function to the corrected biased score to generate the output, and wherein the output comprises an 8 bit unsigned fixed point integer value.