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. 16. The computing system of claim 13 , wherein configuring the first SVM classifier comprises: determining a pair of support vectors from the second plurality of support vectors, wherein the pair comprises the first combined support vector and second combined support vector and wherein the first plurality of support vectors is a subset of the second plurality of support vectors; merging the first combined support vector and the second combined support vector into a third combined support vector; and including the third combined support vector in the first plurality of support vectors.