Brian Mak

2.6k citations
115 papers · 1.6k · h-index 19

Impact in

    • Speech and Audio Processing
    • Music and Audio Processing
    • Blind Source Separation Techniques
    • Speech Recognition and Synthesis
    • Natural Language Processing Techniques

Papers in

Brian Mak

110 papers receiving 1.4k citations

Peers

Brian Mak
Comparison fields: 5 of 107
  • Signal Processing 1.0k
  • Artificial Intelligence 1.1k
  • Human-Computer Interaction 130
  • Computer Vision and Pattern Recognition 261
  • Experimental and Cognitive Psychology 140
Replace Guillaume Gravier with:
Guillaume Gravier France
Dan Ellis United States
John S. Garofolo United States
Thomas Hain United Kingdom
Yasuo Ariki Japan
Oliver Watts United Kingdom
C. Wellekens France
Michiel Bacchiani United States
Seiichi Nakagawa Japan
J. G. Wilpon United States
Brian Mak relative to Guillaume Gravier France Guillaume Gravier's profile →
Citations per field
00.5×3.6×
Guillaume Gravier · 1×
Citations per year

Countries citing papers authored by Brian Mak

Since Specialization
Citations

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

Fields of papers citing papers by Brian Mak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Brian Mak, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Brian Mak Line = papers co-authored together Brian Mak links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 115 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018149
2 1994125
3 200275
4 200272
5 199166
6 200165
7 202264
8 201560
9 201454
10 200350
11 199547
12 202339
13 201839
14 200536
15 199828
16 200727
17 200623
18 200422
19 200621
20 202118

About Brian Mak

Brian Mak is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Human-Computer Interaction, having authored 115 papers that have together received 1.6k indexed citations. Recurring topics across this work include Speech and Audio Processing (78 papers), Speech Recognition and Synthesis (78 papers), Music and Audio Processing (55 papers), Natural Language Processing Techniques (11 papers), Advanced Data Compression Techniques (9 papers), Phonetics and Phonology Research (8 papers), Hand Gesture Recognition Systems (6 papers) and Hearing Impairment and Communication (6 papers). The work is most often cited by research in Signal Processing (1.0k citations), Artificial Intelligence (1.1k citations), Human-Computer Interaction (130 citations), Computer Vision and Pattern Recognition (261 citations) and Experimental and Cognitive Psychology (140 citations). Brian Mak has collaborated with scholars based in Hong Kong, United States and Singapore. Frequent co-authors include Tom Ko, Enrico Bocchieri, Etienne Barnard, J.-C. Junqua, Roger Hsiao, James T. Kwok, David Snyder, Daniel Povey, Jean-Claude Junqua and Wilson Tam. Their work appears in journals such as IEEE Transactions on Speech and Audio Processing, IEEE Transactions on Audio Speech and Language Processing, IEEE/ACM Transactions on Audio Speech and Language Processing, IEEE Signal Processing Letters and Journal of Computer Assisted Learning.

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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