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We propose a rate-distortion based deep neural network (DNN) training algorithm using a smooth matrix functional on the manifold of positive semi-definite matrices as the non-parametric entropy estimator. The objective in the optimization function includes not only the measure of performance of the output layer but also the measure of information distortion between consecutive layers in order to produce...
Children with Autism Spectrum Disorder (ASD) are known to have difficulty in producing and perceiving emotional facial expressions. Their expressions are often perceived as atypical by adult observers. This paper focuses on data driven ways to analyze and quantify atypicality in facial expressions of children with ASD. Our objective is to uncover those characteristics of facial gestures that induce...
Diversity of a classifier ensemble has been shown to benefit overall classification performance. But most conventional methods of training ensembles offer no control on the extent of diversity and are meta-learners. We present a method for creating an ensemble of diverse maximum entropy (∂MaxEnt) models, which are popular in speech and language processing. We modify the objective function for conventional...
Previous approaches to the problem of word fragment detection in speech have focussed primarily on acoustic-prosodic features. This paper proposes that the output of a continuous automatic speech recognition (ASR) system can also be used to derive robust lexical features for the task. We hypothesize that the confusion in the word lattice generated by the ASR system can be exploited for detecting word...
Performance of statistical n-gram language models depends heavily on the amount of training text material and the degree to which the training text matches the domain of interest. The language modeling community is showing a growing interest in using large collections of text (obtainable, for example, from a diverse set of resources on the Internet) to supplement sparse in-domain resources. However,...
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