Brian Strope

4.9k total citations · 1 hit paper
30 papers, 1.5k citations indexed

About

Brian Strope is a scholar working on Artificial Intelligence, Signal Processing and Cognitive Neuroscience. According to data from OpenAlex, Brian Strope has authored 30 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 13 papers in Signal Processing and 8 papers in Cognitive Neuroscience. Recurrent topics in Brian Strope's work include Natural Language Processing Techniques (15 papers), Topic Modeling (14 papers) and Speech and Audio Processing (13 papers). Brian Strope is often cited by papers focused on Natural Language Processing Techniques (15 papers), Topic Modeling (14 papers) and Speech and Audio Processing (13 papers). Brian Strope collaborates with scholars based in United States, Switzerland and South Africa. Brian Strope's co-authors include Ray Kurzweil, Daniel Cer, Yinfei Yang, Steve Yuan, Noah Constant, Chris Tar, Sheng-yi Kong, Nan Hua, A. Alwan and Yun-Hsuan Sung and has published in prestigious journals such as The Journal of the Acoustical Society of America, IEEE Transactions on Speech and Audio Processing and arXiv (Cornell University).

In The Last Decade

Brian Strope

29 papers receiving 1.4k citations

Hit Papers

align trajectories

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.

Universal Sentence Encoder for English

2018 705 citations Daniel Cer, Yinfei Yang et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brian Strope United States	13	1.2k	218	211	186	98	30	1.5k
Martin Rajman Switzerland	14	979 0.8×	122 0.6×	113 0.5×	258 1.4×	63 0.6×	91	1.5k
Thamar Solorio United States	24	1.5k 1.2×	123 0.6×	203 1.0×	468 2.5×	169 1.7×	116	1.9k
Ingrid Zukerman Australia	18	931 0.8×	224 1.0×	93 0.4×	309 1.7×	64 0.7×	121	1.3k
Amanda Stent United States	24	1.7k 1.4×	515 2.4×	296 1.4×	218 1.2×	129 1.3×	87	2.4k
Cynthia Matuszek United States	17	854 0.7×	373 1.7×	72 0.3×	171 0.9×	133 1.4×	55	1.3k
Sheng-yi Kong Taiwan	7	721 0.6×	120 0.6×	70 0.3×	149 0.8×	74 0.8×	12	902
Noah Constant United States	14	2.1k 1.7×	451 2.1×	80 0.4×	275 1.5×	122 1.2×	22	2.5k
Khalid Choukri France	16	1.1k 0.9×	131 0.6×	312 1.5×	95 0.5×	33 0.3×	82	1.3k
Monojit Choudhury India	22	1.3k 1.0×	147 0.7×	84 0.4×	195 1.0×	54 0.6×	130	1.6k
Kuntal Dey India	15	638 0.5×	141 0.6×	87 0.4×	175 0.9×	78 0.8×	54	1.1k

Countries citing papers authored by Brian Strope

Since Specialization

Citations

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

Fields of papers citing papers by Brian Strope

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Brian Strope

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Yang, Yinfei, Daniel Cer, Amin Ahmad, et al.. (2020). Multilingual Universal Sentence Encoder for Semantic Retrieval. 87–94. 228 indexed citations

2.

Yang, Yinfei, Gustavo Hernández Ábrego, Steve Yuan, et al.. (2019). Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax. 5370–5378. 49 indexed citations

3.

Cer, Daniel, Yinfei Yang, Sheng-yi Kong, et al.. (2018). Universal Sentence Encoder for English. 169–174. 705 indexed citations breakdown →

4.

Yang, Yinfei, Steve Yuan, Daniel Cer, et al.. (2018). Learning Semantic Textual Similarity from Conversations. 164–174. 86 indexed citations

5.

Guo, Mandy, Qinlan Shen, Yinfei Yang, et al.. (2018). Effective Parallel Corpus Mining using Bilingual Sentence Embeddings. 165–176. 54 indexed citations

6.

Gouws, Stephan, et al.. (2017). Generating Long and Diverse Responses with Neural Conversation Models. arXiv (Cornell University). 33 indexed citations

7.

Shao, Yuanlong, Stephan Gouws, Denny Britz, et al.. (2017). Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models. 2210–2219. 92 indexed citations

8.

Beaufays, Françoise & Brian Strope. (2013). Language model capitalization. 6749–6752. 12 indexed citations

9.

Jyothi, Preethi, Leif Johnson, Ciprian Chelba, & Brian Strope. (2012). Large-scale discriminative language model reranking for voice-search. North American Chapter of the Association for Computational Linguistics. 41–49. 3 indexed citations

10.

Jyothi, Preethi, Leif Johnson, Ciprian Chelba, & Brian Strope. (2012). Distributed discriminative language models for Google voice-search. 5017–5020. 11 indexed citations

11.

Lin, Hui, Jui-Ting Huang, Françoise Beaufays, Brian Strope, & Yun-Hsuan Sung. (2012). Recognition of multilingual speech in mobile applications. 4881–4884. 8 indexed citations

12.

Strope, Brian, Doug Beeferman, Alexander Gruenstein, & Xin Lei. (2011). Unsupervised testing strategies for ASR. 1685–1688. 11 indexed citations

13.

Sung, Yun-Hsuan, et al.. (2011). Recognizing English queries in Mandarin Voice Search. 49. 5016–5019. 7 indexed citations

14.

Sung, Yun-Hsuan, T. A. Hughes, Françoise Beaufays, & Brian Strope. (2009). Revisiting graphemes with increasing amounts of data. 4449–4452. 6 indexed citations

15.

Strope, Brian, et al.. (2002). A psychoacoustic model for the noise masking of voiceless plosive bursts. 1. 570–573.

16.

Strope, Brian & A. Alwan. (2002). A novel structure to compensate for frequency-dependent loudness recruitment of sensorineural hearing loss. 5. 3539–3542. 2 indexed citations

17.

Strope, Brian & A. Alwan. (2002). Robust word recognition using threaded spectral peaks. 2. 625–628. 13 indexed citations

18.

Strope, Brian & Abeer Alwan. (1998). Amplitude modulation cues for perceptual voicing distinctions in noise. The Journal of the Acoustical Society of America. 103(5_Supplement). 2771–2772. 1 indexed citations

19.

Strope, Brian & A. Alwan. (1997). A model of dynamic auditory perception and its application to robust word recognition. IEEE Transactions on Speech and Audio Processing. 5(5). 451–464. 80 indexed citations

20.

Strope, Brian, et al.. (1997). A psychoacoustic model for the noise masking of plosive bursts. The Journal of the Acoustical Society of America. 101(5). 2789–2802. 9 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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