Liang Lu

59 papers receiving 1.0k citations

Peers

Liang Lu
Comparison fields: 5 of 69
  • Artificial Intelligence 932
  • Signal Processing 851
  • Computational Mechanics 84
  • Computer Vision and Pattern Recognition 72
  • Control and Systems Engineering 38
Replace Tian Tan with:
Tian Tan China
Ehsan Variani United States
Hossein Sameti Iran
Dimitri Kanevsky United States
Björn Hoffmeister Germany
Chao Weng China
Hisashi Kawai Japan
Atsunori Ogawa Japan
Xiao-Lei Zhang China
B. Raj United States
Liang Lu relative to Tian Tan China Tian Tan's profile →
Citations per field
00.5×2.6×
Tian Tan · 1×
Citations per year

Countries citing papers authored by Liang Lu

Since Specialization
Citations

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

Fields of papers citing papers by Liang Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liang Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Liang Lu. A scholar is included among the top collaborators of Liang Lu 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 Liang Lu. Liang Lu 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 18
2 0
3 26
4 9
5 0
6 31
7
Reducing the Latency of End-to-End Streaming Speech Recognition Models with a Scout Network
3
8 30
9 3
10 2
11 14
12 34
13 56
14 21
15
Joint Uncertainty Decoding with Unscented Transform for Noise Robust Subspace Gaussian Mixture Models
1
16 14
17
Maximum negentropy beamforming with superdirectivity
5
18
The France Telecom Orange Labs (Beijing) Video High-level Feature Extraction Systems - TrecVid 2009 Notebook Paper.
2
19 14
20 11

About Liang Lu

Liang Lu is a scholar working on Signal Processing, Artificial Intelligence and Developmental Biology, having authored 64 papers that have together received 1.1k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (52 papers), Speech and Audio Processing (42 papers) and Music and Audio Processing (36 papers). The work is most often cited by research in Signal Processing (851 citations), Artificial Intelligence (932 citations) and Computational Mechanics (84 citations). Liang Lu has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Steve Renals, Jinyu Li, Xingxing Zhang, Arnab Ghoshal, Yifan Gong, Zhong Meng, Naoyuki Kanda, Xiong Xiao, Yashesh Gaur and Takuya Yoshioka. Their work appears in journals such as The Journal of the Acoustical Society of America, IEEE Signal Processing Letters and IEEE Journal of Selected Topics in Signal Processing.

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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