Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
An audio-visual corpus for speech perception and automatic speech recognition
2006755 citationsMartin Cooke, Jon Barker et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Jon Barker's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jon Barker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jon Barker more than expected).
This network shows the impact of papers produced by Jon Barker. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jon Barker. The network helps show where Jon Barker may publish in the future.
Co-authorship network of co-authors of Jon Barker
This figure shows the co-authorship network connecting the top 25 collaborators of Jon Barker.
A scholar is included among the top collaborators of Jon Barker based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Jon Barker. Jon Barker is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Xiong, Feifei, Jon Barker, & Heidi Christensen. (2018). Deep Learning of Articulatory-Based Representations and Applications for Improving Dysarthric Speech Recognition.. 1–5.18 indexed citations
12.
Maddock, Steve, et al.. (2015). Investigating the impact of artificial enhancement of lip visibility on the intelligibility of spectrally-distorted speech.. AVSP. 93–98.1 indexed citations
13.
Vincent, Emmanuel, Jon Barker, Shinji Watanabe, et al.. (2013). The second ‘CHiME’ speech separation and recognition challenge: Datasets, tasks and baselines. HAL (Le Centre pour la Communication Scientifique Directe).11 indexed citations
14.
Ma, Ning, Jon Barker, Heidi Christensen, & Phil Green. (2010). Distant microphone speech recognition in a noisy indoor environment: combining soft missing data and speech fragment decoding.. Conference of the International Speech Communication Association. 19–24.1 indexed citations
15.
Christensen, Heidi & Jon Barker. (2010). Speaker turn tracking with mobile microphones: Combining location and pitch information. European Signal Processing Conference. 954–958.2 indexed citations
16.
Barker, Jon & Xu Shao. (2007). Audio-Visual Speech Fragment Decoding. AVSP.1 indexed citations
Barker, Jon & Frédéric Berthommier. (1999). Estimation of speech acoustics from visual speech features: A comparison of linear and non-linear models.. AVSP. 19.17 indexed citations
20.
Barker, Jon, Frédéric Berthommier, & Jean‐Luc Schwartz. (1998). Is Primitive AV Coherence An Aid To Segment The Scene. AVSP. 103–108.4 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.