Charles Jankowski

507 total citations
11 papers, 282 citations indexed

About

Charles Jankowski is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Charles Jankowski has authored 11 papers receiving a total of 282 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 8 papers in Signal Processing and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Charles Jankowski's work include Speech Recognition and Synthesis (9 papers), Speech and Audio Processing (8 papers) and Music and Audio Processing (6 papers). Charles Jankowski is often cited by papers focused on Speech Recognition and Synthesis (9 papers), Speech and Audio Processing (8 papers) and Music and Audio Processing (6 papers). Charles Jankowski collaborates with scholars based in United States and China. Charles Jankowski's co-authors include Richard P. Lippmann, Sara Basson, Thomas F. Quatieri, D.A. Reynolds, Charles G. Costello, Eric Chang, Ruixi Lin, Jiangjie Chen, Yanghua Xiao and Deqing Yang and has published in prestigious journals such as The Journal of the Acoustical Society of America, IEEE Signal Processing Letters and IEEE Transactions on Speech and Audio Processing.

In The Last Decade

Charles Jankowski

10 papers receiving 246 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Charles Jankowski United States	7	245	218	37	27	22	11	282
Brian A. Hanson United States	9	227 0.9×	175 0.8×	45 1.2×	29 1.1×	17 0.8×	22	258
Wooil Kim United States	11	213 0.9×	161 0.7×	38 1.0×	50 1.9×	12 0.5×	42	287
Sri Harish Mallidi United States	14	362 1.5×	426 2.0×	24 0.6×	25 0.9×	23 1.0×	32	494
Shabnam Ghaffarzadegan United States	12	179 0.7×	178 0.8×	36 1.0×	24 0.9×	20 0.9×	24	325
Chenda Li China	9	297 1.2×	313 1.4×	24 0.6×	16 0.6×	16 0.7×	24	390
Xiaojia Zhao United States	7	358 1.5×	287 1.3×	34 0.9×	12 0.4×	24 1.1×	8	395
Sunil Sivadas Singapore	14	425 1.7×	483 2.2×	43 1.2×	35 1.3×	13 0.6×	29	545
P. Krishnamoorthy India	9	235 1.0×	170 0.8×	33 0.9×	38 1.4×	21 1.0×	27	301
Biing-Hwang Juang United States	6	269 1.1×	257 1.2×	60 1.6×	12 0.4×	15 0.7×	13	333
Akira Kurematsu Japan	7	240 1.0×	212 1.0×	114 3.1×	23 0.9×	13 0.6×	36	382

Countries citing papers authored by Charles Jankowski

Since Specialization

Citations

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

Fields of papers citing papers by Charles Jankowski

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Charles Jankowski

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

All Works

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11 of 11 papers shown

1.

Chen, Jiangjie, et al.. (2023). Distilling Script Knowledge from Large Language Models for Constrained Language Planning. 4303–4325. 6 indexed citations

2.

Lin, Ruixi, et al.. (2019). Optimizing Voice Activity Detection for Noisy Conditions. 2030–2034. 9 indexed citations

3.

Jankowski, Charles, Thomas F. Quatieri, & D.A. Reynolds. (2002). Formant AM-FM for speaker identification. 608–611. 2 indexed citations

4.

Jankowski, Charles, et al.. (2002). NTIMIT: a phonetically balanced, continuous speech, telephone bandwidth speech database. International Conference on Acoustics, Speech, and Signal Processing. 109–112. 120 indexed citations

5.

Lippmann, Richard P., Eric Chang, & Charles Jankowski. (2002). Wordspotter training using figure-of-merit back propagation. i. I/389–I/392. 5 indexed citations

6.

Jankowski, Charles, Thomas F. Quatieri, & D.A. Reynolds. (2002). Fine structure features for speaker identification. 2. 689–692. 11 indexed citations

7.

Jankowski, Charles, Thomas F. Quatieri, & D.A. Reynolds. (2002). Measuring fine structure in speech: application to speaker identification. 1. 325–328. 32 indexed citations

8.

Jankowski, Charles, et al.. (1995). A comparison of signal processing front ends for automatic word recognition. IEEE Transactions on Speech and Audio Processing. 3(4). 286–293. 85 indexed citations

9.

Quatieri, Thomas F., Charles Jankowski, & D.A. Reynolds. (1994). Energy onset times for speaker identification. IEEE Signal Processing Letters. 1(11). 160–162. 7 indexed citations

10.

Jankowski, Charles & Richard P. Lippmann. (1992). Comparison of auditory models for robust speech recognition. 453–453. 4 indexed citations

11.

Basson, Sara, et al.. (1990). The CITRON database: Collection procedures and phonetic analysis of customer elicited speech over the telephone network. The Journal of the Acoustical Society of America. 87(S1). S105–S105. 1 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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