Nearest Neighbors with Learned Distances for Phonetic Frame Classification

Nearest neighbor-based techniques provide an approach to acoustic modeling that avoids the often lengthy and heuristic process of training traditional Gaussian mixturebased models. Here we study the problem of choosing the distance metric for a k-nearest neighbor (k-NN) phonetic frame classifier. We compare the standard Euclidean distance to two learned Mahalanobis distances, based on large-margin nearest neighbors (LMNN) and locality preserving projections (LPP). We use locality sensitive hashing for approximate nearest neighbor search to reduce the test time of k-NN classification. We compare the error rates of these approaches, as well as of baseline Gaussian mixture-based and multilayer perceptron classifiers, on the task of phonetic frame classification of speech from the TIMIT database. The k-NN classifiers outperform Gaussian mixture models, but not multilayer perceptrons. We find that the best k-NN classification performance is obtained using LPP, while LMNN is close behind.