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Alan Wisler
Arizona State University
16Publications
4H-index
44Citations
Publications 16
Newest
Published on Sep 15, 2019 in INTERSPEECH (Conference of the International Speech Communication Association)
Debadatta Dash1
Estimated H-index: 1
(UTD: University of Texas at Dallas),
Alan Wisler4
Estimated H-index: 4
(ASU: Arizona State University)
+ 1 AuthorsJun Wang15
Estimated H-index: 15
(UTD: University of Texas at Dallas)
Published on Feb 1, 2018in IEEE Transactions on Signal Processing5.23
Alan Wisler4
Estimated H-index: 4
(ASU: Arizona State University),
Visar Berisha11
Estimated H-index: 11
(ASU: Arizona State University)
+ 1 AuthorsAlfred O. Hero53
Estimated H-index: 53
(UM: University of Michigan)
A number of fundamental quantities in statistical signal processing and information theory can be expressed as integral functions of two probability density functions. Such quantities are called density functionals as they map density functions onto the real line. For example, information divergence functions measure the dissimilarity between two probability density functions and are useful in a number of applications. Typically, estimating these quantities requires complete knowledge of the und...
Annalise R. Fletcher4
Estimated H-index: 4
(Cant.: University of Canterbury),
Alan Wisler4
Estimated H-index: 4
(ASU: Arizona State University)
+ 2 AuthorsJulie M. Liss24
Estimated H-index: 24
(ASU: Arizona State University)
Purpose Behavioral speech modifications have variable effects on the intelligibility of speakers with dysarthria. In the companion article, a significant relationship was found between measures of speakers' baseline speech and their intelligibility gains following cues to speak louder and reduce rate (Fletcher, McAuliffe, Lansford, Sinex, & Liss, 2017). This study reexamines these features and assesses whether automated acoustic assessments can also be used to predict intelligibility gains. Meth...
Published on Oct 1, 2017 in ASILOMAR (Asilomar Conference on Signals, Systems and Computers)
Prad Kadambi (ASU: Arizona State University), Alan Wisler4
Estimated H-index: 4
(ASU: Arizona State University),
Visar Berisha11
Estimated H-index: 11
(ASU: Arizona State University)
Information divergence functions allow us to measure distances between probability density functions. We focus on the case where we only have data from the two distributions and have no knowledge of the underlying models from which the data is sampled. In this scenario, we consider an f-divergence for which there exists an asymptotically consistent, nonparametric estimator based on minimum spanning trees, the D p divergence. Nonparametric estimators are known to have slow convergence rates in hi...
Published on Aug 20, 2017 in INTERSPEECH (Conference of the International Speech Communication Association)
Visar Berisha11
Estimated H-index: 11
,
Julie M. Liss24
Estimated H-index: 24
+ 3 AuthorsJonathan Eig
Published on 2017in arXiv: Information Theory
Alan Wisler4
Estimated H-index: 4
,
Kevin R. Moon9
Estimated H-index: 9
,
Visar Berisha11
Estimated H-index: 11
Estimating density functionals of analog sources is an important problem in statistical signal processing and information theory. Traditionally, estimating these quantities requires either making parametric assumptions about the underlying distributions or using non-parametric density estimation followed by integration. In this paper we introduce a direct nonparametric approach which bypasses the need for density estimation by using the error rates of k-NN classifiers asdata-driven basis functio...
Published on Jan 1, 2017in arXiv: Information Theory
Alan Wisler4
Estimated H-index: 4
,
Visar Berisha11
Estimated H-index: 11
+ 1 AuthorsAlfred O. Hero53
Estimated H-index: 53
A number of fundamental quantities in statistical signal processing and information theory can be expressed as integral functions of two probability density functions. Such quantities are called density functionals as they map density functions onto the real line. For example, information divergence functions measure the dissimilarity between two probability density functions and are particularly useful in a number of applications. Typically, estimating these quantities requires complete knowled...
Published on Nov 1, 2016in Journal of the Acoustical Society of America1.82
Ming Tu4
Estimated H-index: 4
(ASU: Arizona State University),
Alan Wisler4
Estimated H-index: 4
(ASU: Arizona State University)
+ 1 AuthorsJulie M. Liss24
Estimated H-index: 24
State-of-the-art automatic speech recognition (ASR) engines perform well on healthy speech; however recent studies show that their performance on dysarthric speech is highly variable. This is because of the acoustic variability associated with the different dysarthria subtypes. This paper aims to develop a better understanding of how perceptual disturbances in dysarthric speech relate to ASR performance. Accurate ratings of a representative set of 32 dysarthric speakers along different perceptua...
Published on Jul 1, 2016
Alan Wisler4
Estimated H-index: 4
(ASU: Arizona State University),
Visar Berisha11
Estimated H-index: 11
(ASU: Arizona State University)
+ 1 AuthorsJulie M. Liss24
Estimated H-index: 24
(ASU: Arizona State University)
This paper will investigate viability of a screening application that could be used to identify individuals with Dysarthria from among a larger population using sentence-level speech data. This task presents a number of challenged particularly if we aim to identify the disorder in the earlier stages before the more significant symptoms have begun to manifest themselves. A principal challenge in this task is acheiving robustness to the large number of confounding variables such as gender, age, ac...
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