Ebru Arısoy

1.9k total citations · 1 hit paper
40 papers, 1.0k citations indexed

About

Ebru Arısoy is a scholar working on Artificial Intelligence, Signal Processing and General Health Professions. According to data from OpenAlex, Ebru Arısoy has authored 40 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 7 papers in Signal Processing and 1 paper in General Health Professions. Recurrent topics in Ebru Arısoy's work include Natural Language Processing Techniques (34 papers), Speech Recognition and Synthesis (30 papers) and Topic Modeling (24 papers). Ebru Arısoy is often cited by papers focused on Natural Language Processing Techniques (34 papers), Speech Recognition and Synthesis (30 papers) and Topic Modeling (24 papers). Ebru Arısoy collaborates with scholars based in Türkiye, United States and Austria. Ebru Arısoy's co-authors include Bhuvana Ramabhadran, Brian Kingsbury, Tara N. Sainath, Murat Saraçlar, Vikas Sindhwani, Abhinav Sethy, Mikko Kurimo, Haşim Sak, Vesa Siivola and Antti Puurula and has published in prestigious journals such as Signal Processing, IEEE Transactions on Audio Speech and Language Processing and IEEE/ACM Transactions on Audio Speech and Language Processing.

In The Last Decade

Ebru Arısoy

36 papers receiving 920 citations

Hit Papers

Low-rank matrix factorization for Deep Neural Network tra... 2013 2026 2017 2021 2013 100 200 300
What are hit papers?

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.

  1. Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets
    2013 344 citations Tara N. Sainath, Brian Kingsbury et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ebru Arısoy Türkiye 15 888 242 211 58 37 40 1.0k
Shouxun Lin China 17 799 0.9× 182 0.8× 466 2.2× 45 0.8× 10 0.3× 80 1.2k
Georges Linarès France 10 289 0.3× 175 0.7× 138 0.7× 28 0.5× 25 0.7× 68 497
Hayato Kobayashi Japan 12 257 0.3× 166 0.7× 105 0.5× 47 0.8× 18 0.5× 31 445
Akisato Kimura Japan 14 183 0.2× 102 0.4× 380 1.8× 43 0.7× 31 0.8× 67 602
Bokai Cao United States 15 447 0.5× 39 0.2× 177 0.8× 118 2.0× 40 1.1× 32 700
Thierry Bertin-Mahieux United States 9 302 0.3× 603 2.5× 491 2.3× 161 2.8× 13 0.4× 11 869
Linlin Zong China 13 474 0.5× 63 0.3× 613 2.9× 73 1.3× 12 0.3× 41 868
Spyros Matsoukas United States 23 1.4k 1.6× 647 2.7× 174 0.8× 66 1.1× 30 0.8× 76 1.6k
Long Nguyen United States 19 968 1.1× 512 2.1× 121 0.6× 109 1.9× 19 0.5× 77 1.1k
Wen Wang United States 19 912 1.0× 232 1.0× 167 0.8× 95 1.6× 11 0.3× 70 1.1k

Countries citing papers authored by Ebru Arısoy

Since Specialization
Citations

This map shows the geographic impact of Ebru Arısoy'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 Ebru Arısoy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ebru Arısoy more than expected).

Fields of papers citing papers by Ebru Arısoy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ebru Arısoy. 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 Ebru Arısoy. The network helps show where Ebru Arısoy may publish in the future.

Co-authorship network of co-authors of Ebru Arısoy

This figure shows the co-authorship network connecting the top 25 collaborators of Ebru Arısoy. A scholar is included among the top collaborators of Ebru Arısoy 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 Ebru Arısoy. Ebru Arısoy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Arısoy, Ebru, et al.. (2022). A Framework for Automatic Generation of Spoken Question-Answering Data. 4659–4666. 1 indexed citations
3.
Arısoy, Ebru, et al.. (2021). Uncertainty-Aware Representations for Spoken Question Answering. MEF University Institutional Repository (MEF University). abs 1910 3771. 943–949. 1 indexed citations
4.
5.
Arısoy, Ebru & Murat Saraçlar. (2016). Compositional Neural Network Language Models for Agglutinative Languages. MEF University Institutional Repository (MEF University). 3494–3498. 4 indexed citations
6.
Sainath, Tara N., Brian Kingsbury, Vikas Sindhwani, Ebru Arısoy, & Bhuvana Ramabhadran. (2013). Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets. 6655–6659. 344 indexed citations breakdown →
7.
Sethy, Abhinav, et al.. (2013). Joint training of interpolated exponential n-gram models. 99. 25–30. 4 indexed citations
8.
Arısoy, Ebru, Stanley F. Chen, Bhuvana Ramabhadran, & Abhinav Sethy. (2013). Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition. IEEE/ACM Transactions on Audio Speech and Language Processing. 22(1). 184–192. 22 indexed citations
9.
Arısoy, Ebru, Stanley F. Chen, Bhuvana Ramabhadran, & Abhinav Sethy. (2013). Converting Neural Network Language Models into back-off language models for efficient decoding in automatic speech recognition. 18. 8242–8246. 31 indexed citations
10.
Ramabhadran, Bhuvana, Sanjeev Khudanpur, & Ebru Arısoy. (2012). Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT. North American Chapter of the Association for Computational Linguistics. 22 indexed citations
11.
Arısoy, Ebru, Bhuvana Ramabhadran, & Hong-Kwang Jeff Kuo. (2011). Feature combination approaches for discriminative language models. 617–620. 3 indexed citations
12.
Arısoy, Ebru, Murat Saraçlar, Brian Roark, & Izhak Shafran. (2010). Syntactic and sub-lexical features for Turkish discriminative language models. 5538–5541. 18 indexed citations
13.
Arısoy, Ebru & Murat Saraçlar. (2009). Analysis of the recognition errors in LVCSR of Turkish. 2. 361–364. 1 indexed citations
14.
Arısoy, Ebru, Wolfgang Maier, & Keisuke Inoue. (2008). Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop. 8 indexed citations
15.
Creutz, Mathias, Teemu Hirsimäki, Mikko Kurimo, et al.. (2007). Analysis of Morph-Based Speech Recognition and the Modeling of Out-of-Vocabulary Words Across Languages. North American Chapter of the Association for Computational Linguistics. 380–387. 13 indexed citations
16.
Creutz, Mathias, Teemu Hirsimäki, Mikko Kurimo, et al.. (2007). Morph-based speech recognition and modeling of out-of-vocabulary words across languages. 5(1). 1–29. 92 indexed citations
17.
Arısoy, Ebru, Haşim Sak, & Murat Saraçlar. (2007). Language modeling for automatic turkish broadcast news transcription. 2381–2384. 28 indexed citations
18.
19.
Kurimo, Mikko, Antti Puurula, Ebru Arısoy, et al.. (2006). Unlimited vocabulary speech recognition for agglutinative languages. 487–494. 69 indexed citations
20.
Arısoy, Ebru, Helin Dutağacı, & Levent M. Arslan. (2006). A unified language model for large vocabulary continuous speech recognition of Turkish. Signal Processing. 86(10). 2844–2862. 28 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.

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