Michiel Bacchiani

3.4k citations
66 papers · 1.9k indexed · h-index 24

Michiel Bacchiani

65 papers receiving 1.6k citations

Peers

Michiel Bacchiani
Comparison fields: 5 of 85
  • Artificial Intelligence 1.6k
  • Signal Processing 1.1k
  • Computational Mechanics 151
  • Computer Vision and Pattern Recognition 142
  • Experimental and Cognitive Psychology 74
Replace John S. Garofolo with:
John S. Garofolo United States
David S. Pallett United States
Xavier Anguera Spain
Guillaume Lathoud Switzerland
Timothy J. Hazen United States
Thomas Hain United Kingdom
Chiori Hori Japan
Satoshi Nakamura Japan
Jonathan G. Fiscus United States
Dan Ellis United States
Michiel Bacchiani relative to John S. Garofolo United States John S. Garofolo's profile →
Citations per field
00.5×6.1×
John S. Garofolo · 1×
Citations per year

Countries citing papers authored by Michiel Bacchiani

Since Specialization
Citations

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

Fields of papers citing papers by Michiel Bacchiani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michiel Bacchiani

This figure shows the co-authorship network connecting the top 25 collaborators of Michiel Bacchiani. A scholar is included among the top collaborators of Michiel Bacchiani 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 Michiel Bacchiani. Michiel Bacchiani 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 31
2 13
3 7
4 8
5 101
6 23
7 10
8 144
9 76
10 32
11 6
12 14
13 17
14 11
15 17
16 21
17 5
18 6
19 13
20
AT&T at TREC-8.
33

About Michiel Bacchiani

Michiel Bacchiani is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 66 papers that have together received 1.9k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (50 papers), Speech and Audio Processing (33 papers) and Music and Audio Processing (26 papers). The work is most often cited by research in Signal Processing (1.1k citations), Artificial Intelligence (1.6k citations) and Human-Computer Interaction (49 citations). Michiel Bacchiani has collaborated with scholars based in United States, Japan and Germany. Frequent co-authors include Brian Roark, Tara N. Sainath, Arun Narayanan, Kevin Wilson, Olivier Siohan, K. K. Chin, Ananya Misra, Andrew Senior, Mari Ostendorf and Ehsan Variani. Their work appears in journals such as IEEE Signal Processing Magazine, Speech Communication and IEEE/ACM Transactions on Audio Speech and Language 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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