Joseph Tepperman

595 total citations
26 papers, 451 citations indexed

About

Joseph Tepperman is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology and Developmental and Educational Psychology. According to data from OpenAlex, Joseph Tepperman has authored 26 papers receiving a total of 451 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 12 papers in Experimental and Cognitive Psychology and 7 papers in Developmental and Educational Psychology. Recurrent topics in Joseph Tepperman's work include Speech and dialogue systems (12 papers), Phonetics and Phonology Research (12 papers) and Speech Recognition and Synthesis (11 papers). Joseph Tepperman is often cited by papers focused on Speech and dialogue systems (12 papers), Phonetics and Phonology Research (12 papers) and Speech Recognition and Synthesis (11 papers). Joseph Tepperman collaborates with scholars based in United States. Joseph Tepperman's co-authors include Shrikanth Narayanan, David Traum, S. Narayanan, Matthew Black, Sungbok Lee, Abeer Alwan, Abe Kazemzadeh, Patti Price, Margaret Heritage and Shrikanth Narayanan and has published in prestigious journals such as IEEE Transactions on Audio Speech and Language Processing and Speech Communication.

In The Last Decade

Joseph Tepperman

25 papers receiving 375 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joseph Tepperman United States 12 400 156 118 87 20 26 451
Óscar Saz Spain 11 312 0.8× 78 0.5× 164 1.4× 43 0.5× 18 0.9× 43 410
Joaquim Llisterri Spain 11 228 0.6× 231 1.5× 72 0.6× 63 0.7× 8 0.4× 65 431
Matteo Gerosa Italy 11 572 1.4× 237 1.5× 314 2.7× 104 1.2× 21 1.1× 26 716
Jackson Liscombe United States 10 336 0.8× 224 1.4× 77 0.7× 30 0.3× 10 0.5× 35 480
Loredana Cerrato Ireland 8 185 0.5× 179 1.1× 40 0.3× 89 1.0× 10 0.5× 34 364
Rodolfo Delmonte Italy 11 462 1.2× 106 0.7× 38 0.3× 28 0.3× 18 0.9× 144 541
Anastassia Loukina United States 11 288 0.7× 97 0.6× 58 0.5× 65 0.7× 56 2.8× 43 404
Christine H. Nakatani United States 11 487 1.2× 213 1.4× 63 0.5× 42 0.5× 7 0.3× 25 611
Sheri Hunnicutt Sweden 12 245 0.6× 103 0.7× 85 0.7× 22 0.3× 14 0.7× 35 348
César González-Ferreras Spain 8 143 0.4× 116 0.7× 28 0.2× 59 0.7× 17 0.8× 36 257

Countries citing papers authored by Joseph Tepperman

Since Specialization
Citations

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

Fields of papers citing papers by Joseph Tepperman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joseph Tepperman

This figure shows the co-authorship network connecting the top 25 collaborators of Joseph Tepperman. A scholar is included among the top collaborators of Joseph Tepperman 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 Joseph Tepperman. Joseph Tepperman 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.
Tepperman, Joseph, et al.. (2011). Where should pitch accents and phrase breaks go? a syntax tree transducer solution. 1353–1356. 4 indexed citations
2.
Tepperman, Joseph, et al.. (2010). Testing suprasegmental English through parroting. paper 898–0. 7 indexed citations
3.
Tepperman, Joseph, Sungbok Lee, Shrikanth Narayanan, & Abeer Alwan. (2010). A Generative Student Model for Scoring Word Reading Skills. IEEE Transactions on Audio Speech and Language Processing. 19(2). 348–360. 17 indexed citations
4.
Black, Matthew, Joseph Tepperman, Sungbok Lee, & Shrikanth Narayanan. (2009). Predicting children's reading ability using evaluator-informed features. 1895–1898. 2 indexed citations
5.
Tepperman, Joseph, et al.. (2009). Connecting rhythm and prominence in automatic ESL pronunciation scoring. 684–687. 4 indexed citations
6.
Price, Patti, Joseph Tepperman, Markus Iseli, et al.. (2009). Assessment of emerging reading skills in young native speakers and language learners. Speech Communication. 51(10). 968–984. 15 indexed citations
7.
Black, Matthew, Joseph Tepperman, Abe Kazemzadeh, Sungbok Lee, & Shrikanth Narayanan. (2009). Automatic pronunciation verification of english letter-names for early literacy assessment of preliterate children. 4861–4864. 5 indexed citations
8.
Tepperman, Joseph, Louis Goldstein, Sungbok Lee, & Shrikanth Narayanan. (2009). Automatically rating pronunciation through articulatory phonology. 2771–2774. 3 indexed citations
9.
Tepperman, Joseph & Shrikanth Narayanan. (2008). Better nonnative intonation scores through prosodic theory. 1813–1816. 12 indexed citations
10.
Tepperman, Joseph & Shrikanth Narayanan. (2008). Tree grammars as models of prosodic structure. 2286–2289. 3 indexed citations
11.
Tepperman, Joseph, Matteo Gerosa, & Shrikanth Narayanan. (2008). A generative model for scoring children 2 s reading comprehension.. 16. 2 indexed citations
12.
Black, Matthew, Joseph Tepperman, Sungbok Lee, & Shrikanth Narayanan. (2008). Estimation of children's reading ability by fusion of automatic pronunciation verification and fluency detection. 2779–2782. 10 indexed citations
13.
Black, Matthew, Joseph Tepperman, Abe Kazemzadeh, Sungbok Lee, & Shrikanth Narayanan. (2008). Pronunciation verification of English letter-sounds in preliterate children. 2783–2786. 8 indexed citations
14.
Black, Matthew, Joseph Tepperman, Sungbok Lee, Patti Price, & Shrikanth Narayanan. (2007). Automatic detection and classification of disfluent reading miscues in young children's speech for the purpose of assessment. 206–209. 30 indexed citations
15.
Tepperman, Joseph, Abe Kazemzadeh, & Shrikanth Narayanan. (2007). A text-free approach to assessing nonnative intonation. 2169–2172. 5 indexed citations
16.
Alwan, Abeer, Yijian Bai, Matteo Gerosa, et al.. (2007). A System for Technology Based Assessment of Language and Literacy in Young Children: the Role of Multiple Information Sources. 26–30. 34 indexed citations
17.
Tepperman, Joseph, Matthew Black, Patti Price, et al.. (2007). A Bayesian network classifier for word-level reading assessment. 2185–2188. 17 indexed citations
18.
Tepperman, Joseph, David Traum, & Shrikanth Narayanan. (2006). yeah right: sarcasm recognition for spoken dialogue systems. paper 1821–Wed2BuP.13. 108 indexed citations
19.
Kazemzadeh, Abe, Joseph Tepperman, Jorge Estrela da Silva, et al.. (2006). Automatic detection of voice onset time contrasts for use in pronunciation assessment. paper 1884–Mon3FoP.8. 17 indexed citations
20.
Tepperman, Joseph & S. Narayanan. (2006). Automatic Syllable Stress Detection Using Prosodic Features for Pronunciation Evaluation of Language Learners. 1. 937–940. 53 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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