John F. Pitrelli

28 papers receiving 950 citations

Hit Papers

TOBI: a standard for labeling English prosody19922026200320141992200400600

Peers

John F. Pitrelli
Comparison fields: 5 of 54
  • Artificial Intelligence 843
  • Experimental and Cognitive Psychology 610
  • Signal Processing 242
  • Language and Linguistics 187
  • Linguistics and Language 160
Replace Florian Schiel with:
Florian Schiel Germany
Martine Adda‐Decker France
Kim Silverman United States
Bernd Möbius Germany
Rolf Carlson Sweden
Chilin Shih United States
Morgan Sonderegger Canada
Keelan Evanini United States
P. Price United States
Dafydd Gibbon Germany
John F. Pitrelli relative to Florian Schiel Germany Florian Schiel's profile →
Citations per field
00.5×1.5×
Florian Schiel · 1×
Citations per year

Countries citing papers authored by John F. Pitrelli

Since Specialization
Citations

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

Fields of papers citing papers by John F. Pitrelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John F. Pitrelli. 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 John F. Pitrelli. The network helps show where John F. Pitrelli may publish in the future.

Co-authorship network of co-authors of John F. Pitrelli

This figure shows the co-authorship network connecting the top 25 collaborators of John F. Pitrelli. A scholar is included among the top collaborators of John F. Pitrelli 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 John F. Pitrelli. John F. Pitrelli 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
#WorkIndexed citations
1
Improving Mention Detection Robustness to Noisy Input
20
2
Using Bagging and Boosting Techniques for Improving Coreference Resolution
7
3 9
4 1
5 7
6 84
7 7
8 2
9
A corpus-based approach to expressive speech synthesis.
49
10 4
11
QUANTIFYING THE CONTRIBUTION OF LANGUAGE MODELING TO WRITERINDEPENDENT ONLINE HANDWRITING RECOGNITION
1
12 12
13 6
14 2
15 7
16 3
17 207
18
TOBI: a standard for labeling English prosodybreakdown →
600
19 11
20 6

About John F. Pitrelli

John F. Pitrelli is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology and Computer Vision and Pattern Recognition, having authored 28 papers that have together received 1.2k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (15 papers), Speech and dialogue systems (11 papers) and Speech Recognition and Synthesis (11 papers). The work is most often cited by research in Experimental and Cognitive Psychology (610 citations), Linguistics and Language (160 citations) and Artificial Intelligence (843 citations). John F. Pitrelli has collaborated with scholars based in United States. Frequent co-authors include Julia Hirschberg, Mary E. Beckman, Kim Silverman, Colin W. Wightman, Patti Price, Janet B. Pierrehumbert, Ellen Eide, Wael Hamza, R. Bakis and Michael Picheny. Their work appears in journals such as Scientific American, IEEE Transactions on Audio Speech and Language Processing and International Journal on Document Analysis and Recognition (IJDAR).

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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2026