D
DeLiang Wang
Researcher at Ohio State University
Publications - 475
Citations - 28623
DeLiang Wang is an academic researcher from Ohio State University. The author has contributed to research in topics: Speech processing & Speech enhancement. The author has an hindex of 82, co-authored 440 publications receiving 23687 citations. Previous affiliations of DeLiang Wang include Massachusetts Institute of Technology & Tsinghua University.
Papers
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Journal ArticleDOI
Image and Texture Segmentation Using Local Spectral Histograms
Xiuwen Liu,DeLiang Wang +1 more
TL;DR: A method for segmenting images consisting of texture and nontexture regions based on local spectral histograms using local spectral Histograms of homogeneous regions and an algorithm that iteratively updates the segmentation using the derived probability models.
Journal ArticleDOI
Investigation of speech separation as a front-end for noise robust speech recognition
Arun Narayanan,DeLiang Wang +1 more
TL;DR: An in-depth evaluation of such techniques as a front-end for noise-robust automatic speech recognition (ASR) and a diagonal feature discriminant linear regression (dFDLR) adaptation that can be performed on a per-utterance basis for ASR systems employing deep neural networks and HMM are performed.
Proceedings ArticleDOI
On the optimality of ideal binary time-frequency masks
Yipeng Li,DeLiang Wang +1 more
TL;DR: The results show that ideal binary masks are close in performance to ideal ratio masks which are closely related to the Wiener filter, the theoretically optimal linear filter.
Journal ArticleDOI
Deep Learning Based Binaural Speech Separation in Reverberant Environments
Xueliang Zhang,DeLiang Wang +1 more
TL;DR: Systematic evaluations and comparisons show that the proposed system achieves very good separation performance and substantially outperforms related algorithms under challenging multisource and reverberant environments.
Journal ArticleDOI
A binaural processor for missing data speech recognition in the presence of noise and small-room reverberation
TL;DR: The binaural auditory model improves speech recognition performance in small room reverberation conditions in the presence of spatially separated noise, particularly for conditions in which the spatial separation is 20� or larger.