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.
Speaker identification on the SCOTUS corpus
2008415 citationsJiahong Yuan, Mark Libermanprofile →
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 Mark Liberman'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 Mark Liberman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Liberman more than expected).
This network shows the impact of papers produced by Mark Liberman. 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 Mark Liberman. The network helps show where Mark Liberman may publish in the future.
Co-authorship network of co-authors of Mark Liberman
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Liberman.
A scholar is included among the top collaborators of Mark Liberman 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 Mark Liberman. Mark Liberman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cieri, Christopher, et al.. (2018). Introducing NIEUW: Novel Incentives and Workflows for Eliciting Linguistic Data.. Language Resources and Evaluation.1 indexed citations
10.
Kuang, Jianjing & Mark Liberman. (2015). Influence of spectral cues on the perception of pitch height.. ICPhS.10 indexed citations
11.
Wang, Wen, Andreas Stolcke, Jiahong Yuan, & Mark Liberman. (2013). A Cross-language Study on Automatic Speech Disfluency Detection. North American Chapter of the Association for Computational Linguistics. 703–708.4 indexed citations
12.
Yuan, Jiahong & Mark Liberman. (2011). Automatic Measurement and Comparison of Vowel Nasalization across Languages.. ICPhS. 2244–2247.11 indexed citations
13.
Cieri, Christopher & Mark Liberman. (2008). 15 Years of Language Resource Creation and Sharing: a Progress Report on LDC Activities. Language Resources and Evaluation.5 indexed citations
Cieri, Christopher & Mark Liberman. (2006). More Data and Tools for More Languages and Research Areas: A Progress Report on LDC Activities.. Language Resources and Evaluation. 779–782.7 indexed citations
16.
Liberman, Mark, et al.. (2001). Tonal Complexes and Tonal Alignment. Scholarworks (University of Massachusetts Amherst). 31(1). 2.21 indexed citations
17.
Cieri, Christopher & Mark Liberman. (2000). Issues in Corpus Creation and Distribution: The Evolution of the Linguistic Data Consortium. Language Resources and Evaluation.6 indexed citations
18.
Barras, Claude, Edouard Geoffrois, Zhibiao Wu, & Mark Liberman. (1998). Transcriber: a free tool for segmenting, labeling and transcribing speech. Language Resources and Evaluation. 1373–1376.41 indexed citations
19.
Liberman, Mark. (1994). Commentary on Kaplan and Kay. Computational Linguistics. 20(3). 379–379.2 indexed citations
20.
Liberman, Mark. (1994). Computer speech synthesis: its status and prospects. Europe PMC (PubMed Central). 107–115.2 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.