Chi-Chun Lee

7.2k total citations · 1 hit paper
195 papers, 4.8k citations indexed

About

Chi-Chun Lee is a scholar working on Experimental and Cognitive Psychology, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Chi-Chun Lee has authored 195 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 86 papers in Experimental and Cognitive Psychology, 74 papers in Artificial Intelligence and 56 papers in Signal Processing. Recurrent topics in Chi-Chun Lee's work include Emotion and Mood Recognition (81 papers), Speech and Audio Processing (42 papers) and Music and Audio Processing (33 papers). Chi-Chun Lee is often cited by papers focused on Emotion and Mood Recognition (81 papers), Speech and Audio Processing (42 papers) and Music and Audio Processing (33 papers). Chi-Chun Lee collaborates with scholars based in Taiwan, United States and Australia. Chi-Chun Lee's co-authors include Shrikanth Narayanan, Carlos Busso, Sungbok Lee, Emily Mower, Abe Kazemzadeh, Murtaza Bulut, Samuel Kim, Daniel Bone, Matthew Black and Panayiotis Georgiou and has published in prestigious journals such as Blood, PLoS ONE and Proceedings of the IEEE.

In The Last Decade

Chi-Chun Lee

176 papers receiving 4.5k 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.

IEMOCAP: interactive emotional dyadic motion capture database

2008 2.2k citations Carlos Busso, Chi-Chun Lee et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chi-Chun Lee Taiwan	26	2.8k	2.3k	1.7k	718	688	195	4.8k
Sungbok Lee United States	31	4.3k 1.5×	3.4k 1.5×	2.7k 1.6×	822 1.1×	913 1.3×	122	6.5k
Anton Batliner Germany	37	3.5k 1.2×	3.6k 1.6×	2.8k 1.7×	527 0.7×	759 1.1×	189	6.3k
Stefan Steidl Germany	36	2.6k 0.9×	2.6k 1.1×	2.3k 1.4×	347 0.5×	670 1.0×	102	5.5k
Erik Marchi Germany	20	1.1k 0.4×	1.2k 0.5×	1.2k 0.7×	462 0.6×	406 0.6×	52	2.6k
Martin Wöllmer Germany	28	2.6k 0.9×	2.6k 1.1×	2.2k 1.3×	492 0.7×	969 1.4×	80	5.0k
Nicholas Cummins Germany	31	1.7k 0.6×	1.1k 0.5×	917 0.5×	527 0.7×	326 0.5×	119	3.3k
Satoshi Nakamura Japan	35	520 0.2×	3.7k 1.6×	2.4k 1.4×	974 1.4×	938 1.4×	677	6.5k
Roddy Cowie United Kingdom	38	5.1k 1.8×	2.4k 1.1×	2.0k 1.2×	1.5k 2.1×	1.8k 2.6×	131	7.2k
Alessandro Vinciarelli United Kingdom	32	1.5k 0.5×	2.0k 0.9×	858 0.5×	503 0.7×	1.7k 2.5×	182	4.8k
Abe Kazemzadeh United States	17	2.6k 0.9×	2.2k 1.0×	1.5k 0.9×	379 0.5×	653 0.9×	36	3.9k

Countries citing papers authored by Chi-Chun Lee

Since Specialization

Citations

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

Fields of papers citing papers by Chi-Chun Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chi-Chun Lee. 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 Chi-Chun Lee. The network helps show where Chi-Chun Lee may publish in the future.

Co-authorship network of co-authors of Chi-Chun Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Chi-Chun Lee. A scholar is included among the top collaborators of Chi-Chun Lee 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 Chi-Chun Lee. Chi-Chun Lee 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

Katz, William F., et al.. (2025). Phonetically-Anchored Domain Adaptation for Cross-Lingual Speech Emotion Recognition. IEEE Transactions on Affective Computing. 16(3). 1631–1645.

Chen, Huanyu, et al.. (2025). Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation. PubMed. 4. e67239–e67239.

Lee, Chi-Chun, et al.. (2024). Minority Views Matter: Evaluating Speech Emotion Classifiers With Human Subjective Annotations by an All-Inclusive Aggregation Rule. IEEE Transactions on Affective Computing. 16(1). 41–55. 5 indexed citations

Sung, Chih‐Wei, et al.. (2024). The unreliability of crackles: insights from a breath sound study using physicians and artificial intelligence. npj Primary Care Respiratory Medicine. 34(1). 28–28. 1 indexed citations

Lee, Chi-Chun, et al.. (2024). Learning With Rater-Expanded Label Space to Improve Speech Emotion Recognition. IEEE Transactions on Affective Computing. 15(3). 1539–1552. 3 indexed citations

Lee, Chi-Chun, et al.. (2023). Comparison of contrast-enhanced ultrasonography and MRI results obtained by expert and novice radiologists indicating short-term response after transarterial chemoembolisation for hepatocellular carcinoma. Clinical Radiology. 79(1). e73–e79. 1 indexed citations

Lin, Wei-Cheng, et al.. (2022). Monologue versus Conversation: Differences in Emotion Perception and Acoustic Expressivity. 1–7. 3 indexed citations

Monaghan, Sara A., Yen‐Chun Liu, Michael Boyiadzis, et al.. (2021). A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data. American Journal of Clinical Pathology. 157(4). 546–553. 26 indexed citations

Lee, Chi-Chun, et al.. (2020). “Your Behavior Makes Me Think It Is a Lie”: Recognizing Perceived Deception using Multimodal Data in Dialog Games. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 393–402. 2 indexed citations

10.

Chen, Hsiang-Chun, et al.. (2020). Sensing with Contexts: Crying Reason Classification for Infant Care Center with Environmental Fusion. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 314–318. 1 indexed citations

11.

Wang, Yu‐Fen, et al.. (2019). Learning a Cytometric Deep Phenotype Embedding for Automatic Hematological Malignancies Classification. PubMed. 2019. 1733–1736. 8 indexed citations

12.

Lee, Chi-Chun, et al.. (2019). Learning Minimal Intra-Genre Multimodal Embedding from Trailer Content and Reactor Expressions for Box Office Prediction. 3 indexed citations

13.

Wang, Yu‐Fen, Bor‐Sheng Ko, Chi‐Cheng Li, et al.. (2017). An Artificial Intelligence Approach for B Lymphoblastic Leukemia Minimal Residual Disease Detection and Clinical Prognosis Prediction Using Flow Cytometry Data. Blood. 130. 3980–3980. 1 indexed citations

14.

Li, Chi‐Cheng, Bor‐Sheng Ko, Yu‐Fen Wang, et al.. (2017). An Artificial Intelligence Approach for Predicting Allogeneic Hematopoietic Stem Cell Transplantation Outcome By Detecting Pre-Transplant Minimal Residual Disease in Acute Myeloid Leukemia Using Flow Cytometry Data. Blood. 130. 2042–2042. 1 indexed citations

15.

Lee, Chi-Chun, et al.. (2017). Embedding stacked bottleneck vocal features in a LSTM architecture for automatic pain level classification during emergency triage. 313–318. 25 indexed citations

16.

Hsu, Ya-Ling, et al.. (2016). Toward Development and Evaluation of Pain Level-Rating Scale for Emergency Triage based on Vocal Characteristics and Facial Expressions. 92–96. 22 indexed citations

17.

Chaspari, Theodora, Daniel Bone, James Gibson, Chi-Chun Lee, & Shrikanth Narayanan. (2013). Using physiology and language cues for modeling verbal response latencies of children with ASD. 3702–3706. 12 indexed citations

18.

Bone, Daniel, Matthew Black, Chi-Chun Lee, et al.. (2012). Spontaneous-speech acoustic-prosodic features of children with autism and the interacting psychologist. 1043–1046. 45 indexed citations

19.

Lee, Chi-Chun, Athanasios Katsamanis, Brian R. Baucom, Panayiotis Georgiou, & Shrikanth Narayanan. (2012). Using measures of vocal entrainment to inform outcome-related behaviors in marital conflicts. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 1–5. 3 indexed citations

20.

Black, Matthew, Athanasios Katsamanis, Chi-Chun Lee, et al.. (2010). Automatic classification of married couples' behavior using audio features. 2030–2033. 42 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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