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.
This map shows the geographic impact of Tanja Schultz'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 Tanja Schultz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tanja Schultz more than expected).
This network shows the impact of papers produced by Tanja Schultz. 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 Tanja Schultz. The network helps show where Tanja Schultz may publish in the future.
Co-authorship network of co-authors of Tanja Schultz
This figure shows the co-authorship network connecting the top 25 collaborators of Tanja Schultz.
A scholar is included among the top collaborators of Tanja Schultz 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 Tanja Schultz. Tanja Schultz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Abate, Solomon Teferra, et al.. (2020). DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech. 265–270.1 indexed citations
7.
Schultz, Tanja, et al.. (2020). Building Language Models for Morphological Rich Low-Resource Languages using Data from Related Donor Languages: the Case of Uyghur. 271–276.3 indexed citations
8.
Schultz, Tanja, et al.. (2020). Automatic Speech Recognition for Uyghur through Multilingual Acoustic Modeling.. Language Resources and Evaluation. 6444–6449.3 indexed citations
9.
Schultz, Tanja. (2019). Biosignal Processing for Human-Machine Interaction.. Conference of the International Speech Communication Association.1 indexed citations
10.
Diener, Lorenz, et al.. (2018). Session-Independent Array-Based EMG-to-Speech Conversion using Convolutional Neural Networks.. 1–5.8 indexed citations
Schultz, Tanja & Tim Schlippe. (2014). GlobalPhone: Pronunciation Dictionaries in 20 Languages. Language Resources and Evaluation. 337–341.8 indexed citations
13.
Schlippe, Tim, et al.. (2014). Automatic Detection of Anglicisms for the Pronunciation Dictionary Generation: A Case Study on our German IT Corpus. 207–214.3 indexed citations
14.
Schlippe, Tim, et al.. (2014). Combining Grapheme-to-Phoneme Converter Outputs for Enhanced Pronunciation Generation in Low-Resource Scenarios. 139–145.7 indexed citations
15.
Amma, Christoph, et al.. (2013). Activity Recognition for an Intelligent Knee Orthosis.5 indexed citations
16.
Schultz, Tanja & Alan W. Black. (2008). Rapid Language Adaptation Tools and Technologies for Multilingual Speech Processing. International Conference on Acoustics, Speech, and Signal Processing.16 indexed citations
17.
Sidner, Candace L., Tanja Schultz, Matthew Stone, & ChengXiang Zhai. (2007). Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference. North American Chapter of the Association for Computational Linguistics.56 indexed citations
Schultz, Tanja. (1987). Education investments and returns in economic development. Econstor (Econstor).6 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.