Da-Rong Liu

852 total citations · 1 hit paper
9 papers, 508 citations indexed

About

Da-Rong Liu is a scholar working on Artificial Intelligence, Signal Processing and Infectious Diseases. According to data from OpenAlex, Da-Rong Liu has authored 9 papers receiving a total of 508 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 8 papers in Signal Processing and 0 papers in Infectious Diseases. Recurrent topics in Da-Rong Liu's work include Speech Recognition and Synthesis (9 papers), Speech and Audio Processing (7 papers) and Music and Audio Processing (6 papers). Da-Rong Liu is often cited by papers focused on Speech Recognition and Synthesis (9 papers), Speech and Audio Processing (7 papers) and Music and Audio Processing (6 papers). Da-Rong Liu collaborates with scholars based in Taiwan, United Kingdom and United States. Da-Rong Liu's co-authors include Hung-yi Lee, Andy T. Liu, Shinji Watanabe, Shuyan Dong, Shang-Wen Li, Yist Y. Lin, Shu-Wen Yang, Po-Han Chi, Jiatong Shi and Cheng-I Lai and has published in prestigious journals such as IEEE/ACM Transactions on Audio Speech and Language Processing.

In The Last Decade

Da-Rong Liu

9 papers receiving 485 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.

SUPERB: Speech Processing Universal PERformance Benchmark

2021 409 citations Shu-Wen Yang, Po-Han Chi et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Da-Rong Liu Taiwan	7	440	300	61	24	14	9	508
Yist Y. Lin Taiwan	4	410 0.9×	286 1.0×	59 1.0×	27 1.1×	15 1.1×	5	472
Po-Han Chi Taiwan	2	439 1.0×	293 1.0×	69 1.1×	42 1.8×	15 1.1×	3	527
Zili Huang China	10	626 1.4×	499 1.7×	64 1.0×	26 1.1×	14 1.0×	21	720
Andy T. Liu Taiwan	7	593 1.3×	392 1.3×	85 1.4×	51 2.1×	21 1.5×	12	699
Kyu J. Han United States	12	371 0.8×	311 1.0×	50 0.8×	44 1.8×	11 0.8×	38	456
Aaron Lawson United States	11	439 1.0×	413 1.4×	19 0.3×	40 1.7×	8 0.6×	41	500
Javier Latorre United Kingdom	13	497 1.1×	377 1.3×	71 1.2×	51 2.1×	10 0.7×	35	554
Masami Akamine Japan	11	321 0.7×	297 1.0×	45 0.7×	52 2.2×	8 0.6×	45	387
Ravichander Vipperla France	11	272 0.6×	224 0.7×	37 0.6×	41 1.7×	38 2.7×	23	348
Rob Clark United States	7	479 1.1×	327 1.1×	27 0.4×	64 2.7×	15 1.1×	11	565

Countries citing papers authored by Da-Rong Liu

Since Specialization

Citations

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

Fields of papers citing papers by Da-Rong Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Da-Rong Liu. 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 Da-Rong Liu. The network helps show where Da-Rong Liu may publish in the future.

Co-authorship network of co-authors of Da-Rong Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Da-Rong Liu. A scholar is included among the top collaborators of Da-Rong Liu 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 Da-Rong Liu. Da-Rong Liu 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:

9 of 9 papers shown

1.

Liu, Da-Rong, et al.. (2023). Parallel Synthesis for Autoregressive Speech Generation. IEEE/ACM Transactions on Audio Speech and Language Processing. 31. 3095–3111. 2 indexed citations

2.

Liu, Da-Rong, et al.. (2022). Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech. IEEE/ACM Transactions on Audio Speech and Language Processing. 30. 1558–1571. 26 indexed citations

3.

Liu, Da-Rong, et al.. (2021). Learning Phone Recognition From Unpaired Audio and Phone Sequences Based on Generative Adversarial Network. IEEE/ACM Transactions on Audio Speech and Language Processing. 30. 230–243. 5 indexed citations

4.

Yang, Shu-Wen, Po-Han Chi, Yung-Sung Chuang, et al.. (2021). SUPERB: Speech Processing Universal PERformance Benchmark. 1194–1198. 409 indexed citations breakdown →

5.

Liu, Da-Rong, et al.. (2021). Stabilizing Label Assignment for Speech Separation by Self-Supervised Pre-Training. 3056–3060. 7 indexed citations

6.

Liu, Da-Rong, Chunxi Liu, Frank Zhang, et al.. (2020). Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model. 3650–3654. 8 indexed citations

7.

Chen, Kuan‐Yu, et al.. (2019). Completely Unsupervised Phoneme Recognition by a Generative Adversarial Network Harmonized with Iteratively Refined Hidden Markov Models. 15 indexed citations

8.

Liu, Da-Rong, Kuan‐Yu Chen, Hung-yi Lee, & Lin-shan Lee. (2018). Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings. 22 indexed citations

9.

Liu, Da-Rong, et al.. (2018). Improving Unsupervised Style Transfer in end-to-end Speech Synthesis with end-to-end Speech Recognition. 640–647. 14 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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