Recognizing aspects of articulation from audio recordings of speech is an important problem, either as an end in itself or as part of an articulatory approach to automatic speech recognition. In this paper we study the frame-level classification of a set of articulatory features (AFs) inspired by the vocal tract variables of articulatory phonology. We compare k nearest neighbor (k-NN) classifie...

Estimating entropy and mutual information consistently is important for many machine learning applications. The Kozachenko-Leonenko (KL) estimator (Kozachenko & Leonenko, 1987) is a widely used nonparametric estimator for the entropy of multivariate continuous random variables, as well as the basis of the mutual information estimator of Kraskov et al. (2004), perhaps the most widely used estima...

While k-d trees have been widely studied and used, their theoretical advantages are often not realized due to ineffective search strategies and generally poor performance in high dimensional spaces. In this paper we outline an effective search algorithm for k-d trees that combines an optimal depth-first branch and bound (DFBB) strategy with a unique method for path ordering and pruning. Our ini...

This paper presents a novel Nearest Neighbor (NN) classifier. NN classification is a well studied method for pattern classification having the following properties; * it performs maximum-margin classification and achieves less than the twice of ideal Bayesian error, * it does not require the knowledge on pattern distributions, kernel functions or base classifiers, and * it can naturally be appl...

In this paper, we introduce an implementation of the attribute selection algorithm, Correlation-based Feature Selection (CFS) integrated with our k-nearest neighbour (k-NN) framework. Binary neural networks underpin our k-NN and allow us to create a unified framework for attribute selection, prediction and classification. We apply the framework to a real world application of predicting bus jour...

K-nearest neighbor (k-NN) classification is a powerful and simple method for classification. k-NN classifiers approximate a Bayesian classifier for a large number of data samples. The accuracy of k-NN classifier relies on the distance metric used for calculating nearest neighbor and features used for instances in training and testing data. In this paper we use deep neural networks (DNNs) as a f...