Match!
Alan Wisler
Arizona State University
16Publications
4H-index
59Citations
Publications 16
Newest
Sep 15, 2019 in INTERSPEECH (Conference of the International Speech Communication Association)
#1Debadatta Dash (UTD: University of Texas at Dallas)H-Index: 3
#2Alan Wisler (ASU: Arizona State University)H-Index: 4
Last.Jun Wang (UTD: University of Texas at Dallas)H-Index: 15
view all 4 authors...
Source
Source
#1Alan WislerH-Index: 4
Last.Jun WangH-Index: 15
view all 7 authors...
Source
#1Alan Wisler (ASU: Arizona State University)H-Index: 4
#2Visar Berisha (ASU: Arizona State University)H-Index: 12
Last.Alfred O. Hero (UM: University of Michigan)H-Index: 59
view all 4 authors...
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...
2 CitationsSource
#1Annalise R. Fletcher (Cant.: University of Canterbury)H-Index: 4
#2Alan Wisler (ASU: Arizona State University)H-Index: 4
Last.Julie M. Liss (ASU: Arizona State University)H-Index: 26
view all 5 authors...
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 ...
1 CitationsSource
Oct 1, 2017 in ASILOMAR (Asilomar Conference on Signals, Systems and Computers)
#1Prad Kadambi (ASU: Arizona State University)
#2Alan Wisler (ASU: Arizona State University)H-Index: 4
Last.Visar Berisha (ASU: Arizona State University)H-Index: 12
view all 3 authors...
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...
Source
Aug 20, 2017 in INTERSPEECH (Conference of the International Speech Communication Association)
#1Visar BerishaH-Index: 12
#2Julie M. LissH-Index: 26
view all 6 authors...
Source
#1Alan WislerH-Index: 4
#2Kevin R. MoonH-Index: 10
Last.Visar BerishaH-Index: 12
view all 3 authors...
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...
2 Citations
#1Alan WislerH-Index: 4
#2Visar BerishaH-Index: 12
Last.Alfred O. HeroH-Index: 59
view all 4 authors...
#1Ming Tu (ASU: Arizona State University)H-Index: 5
#2Alan Wisler (ASU: Arizona State University)H-Index: 4
Last.Julie M. LissH-Index: 26
view all 4 authors...
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...
5 CitationsSource
12