13. A computing system comprising: at least one processor; a memory including instructions operable to be executed by the at least one processor to cause the system to perform a set of actions comprising: determining a first plurality of feature dimensions, wherein the determining is based on a respective utility of each feature dimension for classifying whether audio data includes a keyword; configuring a first support vector machine (SVM) classifier, the first SVM classifier comprising a first plurality of support vectors, each of the first plurality of support vectors including a value for each of the first plurality of feature dimensions; combining a positive pair of support vectors in the first plurality of support vectors into a first combined support vector, wherein each support vector of the positive pair of support vectors being on a positive side of a binary classification of the SVM classifier; combining a negative pair of support vectors in the first plurality of support vectors into a second combined support vector, each support vector of the negative pair of support vectors being on a negative side of the binary classification of the SVM classifier; creating additional combined support vectors by alternating between combining a positive pair of support vectors and combining a negative pair of support vectors; determining a second plurality of support vectors including at least the first combined support vector, the second combined support vector, and the additional combined support vectors; and configuring a second SVM classifier comprising the second plurality of support vectors.
13. A computing system comprising: at least one processor; a memory including instructions operable to be executed by the at least one processor to cause the system to perform a set of actions comprising: determining a first plurality of feature dimensions, wherein the determining is based on a respective utility of each feature dimension for classifying whether audio data includes a keyword; configuring a first support vector machine (SVM) classifier, the first SVM classifier comprising a first plurality of support vectors, each of the first plurality of support vectors including a value for each of the first plurality of feature dimensions; combining a positive pair of support vectors in the first plurality of support vectors into a first combined support vector, wherein each support vector of the positive pair of support vectors being on a positive side of a binary classification of the SVM classifier; combining a negative pair of support vectors in the first plurality of support vectors into a second combined support vector, each support vector of the negative pair of support vectors being on a negative side of the binary classification of the SVM classifier; creating additional combined support vectors by alternating between combining a positive pair of support vectors and combining a negative pair of support vectors; determining a second plurality of support vectors including at least the first combined support vector, the second combined support vector, and the additional combined support vectors; and configuring a second SVM classifier comprising the second plurality of support vectors. 17. The computing system of claim 13 , the set of actions further comprising determining the positive pair of support vectors to have a closest Euclidean distance to each other in a feature space from among other pairs of support vectors on the positive side of the binary classification.