Sarah Borys

417 total citations
13 papers, 294 citations indexed

About

Sarah Borys is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology and Signal Processing. According to data from OpenAlex, Sarah Borys has authored 13 papers receiving a total of 294 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 9 papers in Experimental and Cognitive Psychology and 8 papers in Signal Processing. Recurrent topics in Sarah Borys's work include Speech Recognition and Synthesis (10 papers), Phonetics and Phonology Research (9 papers) and Speech and Audio Processing (8 papers). Sarah Borys is often cited by papers focused on Speech Recognition and Synthesis (10 papers), Phonetics and Phonology Research (9 papers) and Speech and Audio Processing (8 papers). Sarah Borys collaborates with scholars based in United States. Sarah Borys's co-authors include Mark Hasegawa‐Johnson, Camille Goudeseune, Thomas S. Huang, Bowon Lee, Ming Liu, Ken Chen, Jennifer Cole, A. S. Cohen, Tae-Jin Yoon and Katrin Kirchhoff and has published in prestigious journals such as IEEE Transactions on Audio Speech and Language Processing, Speech Communication and Genetic Testing and Molecular Biomarkers.

In The Last Decade

Sarah Borys

13 papers receiving 251 citations

Peers

Sarah Borys

SP

AI

ECP

CVPR

CN

Sorin Dusan United States

Slobodan T. Jovičić Serbia

Michael S. Scordilis United States

Panikos Heracleous Japan

C. Ris Belgium

Harald Höge Germany

Ulpu Remes Finland

Akira Sasou Japan

Yasunari Obuchi Japan

Lorenzo Fissore

Sorin Dusan United States

Sarah Borys

231 ×1.1

SP

236 ×1.2

AI

76 ×0.7

ECP

44 ×1.0

CVPR

25 ×1.3

CN

Citations per year, relative to Sarah Borys Sarah Borys (= 1×) peers Sorin Dusan

Countries citing papers authored by Sarah Borys

Since Specialization

Citations

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

Fields of papers citing papers by Sarah Borys

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarah Borys

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

All Works

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13 of 13 papers shown

1.

Borys, Sarah, et al.. (2009). SVM-HMM LANDMARK BASED SPEECH RECOGNITION. Genetic Testing and Molecular Biomarkers. 13(5). 631–4. 2 indexed citations

2.

Hasegawa‐Johnson, Mark, Jennifer Cole, Ken Chen, et al.. (2009). Prosodic Hierarchy as an Organizing Framework for the Sources of Context in Phone-Based and Articulatory-Feature-Based Speech Recognition. 101–128. 2 indexed citations

3.

Hasegawa‐Johnson, Mark, James Baker, Sarah Borys, et al.. (2006). Landmark-Based Speech Recognition: Report of the 2004 Johns Hopkins Summer Workshop. PubMed. 1(1415088). 213–216. 67 indexed citations

4.

Borys, Sarah & Mark Hasegawa‐Johnson. (2005). Distinctive feature based SVM discriminant features for improvements to phone recognition on telephone band speech. 697–700. 8 indexed citations

5.

Chen, Ken, Mark Hasegawa‐Johnson, A. S. Cohen, et al.. (2005). Prosody dependent speech recognition on radio news corpus of American English. IEEE Transactions on Audio Speech and Language Processing. 14(1). 232–245. 29 indexed citations

6.

Hasegawa‐Johnson, Mark, Ken Chen, Jennifer Cole, et al.. (2005). Simultaneous recognition of words and prosody in the Boston University Radio Speech Corpus. Speech Communication. 46(3-4). 418–439. 34 indexed citations

7.

Hasegawa‐Johnson, Mark, Jennifer Cole, Chilin Shih, et al.. (2004). Speech Recognition Models of the Interdependence Among Syntax, Prosody, and Segmental Acoustics. North American Chapter of the Association for Computational Linguistics. 56–63. 13 indexed citations

8.

Lee, Bowon, Mark Hasegawa‐Johnson, Camille Goudeseune, et al.. (2004). AVICAR: audio-visual speech corpus in a car environment. 116 indexed citations

9.

Hasegawa‐Johnson, Mark, et al.. (2004). Stop consonant classification by dynamic formant trajectory. 2481–2484. 9 indexed citations

10.

Borys, Sarah, A. S. Cohen, Mark Hasegawa‐Johnson, & Jennifer Cole. (2004). Modeling and recognition of phonetic and prosodic factors for improvements to acoustic speech recognition models. 3013–3016. 1 indexed citations

11.

Borys, Sarah & Mark Hasegawa‐Johnson. (2003). Recognition of Prosodic Factors and Detection of Landmarks for Improvements to Continuous Speech Recognition Systems. 1 indexed citations

12.

Borys, Sarah. (2003). The importance of prosodic factors in phoneme modeling with applications to speech recognition. 3. 7–12. 3 indexed citations

13.

Chen, Ken, Sarah Borys, Mark Hasegawa‐Johnson, & Jennifer Cole. (2003). Prosody dependent speech recognition with explicit duration modelling at intonational phrase boundaries. 393–396. 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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