Daniel Gálvez

1.2k citations
23 papers · 718 · 1 hit paper · h-index 8

Impact in

    • Speech and Audio Processing
    • Music and Audio Processing
    • Speech Recognition and Synthesis
    • Natural Language Processing Techniques
    • Topic Modeling
    • Speech and dialogue systems

Papers in

Daniel Gálvez

21 papers receiving 634 citations

Daniel Gálvez's Hit Papers

Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI 2016 · 458 citations
4580+3+6Years since publication100200300400

Peers

Daniel Gálvez
Comparison fields: 5 of 84
  • Signal Processing 318
  • Artificial Intelligence 457
  • Strategy and Management 136
  • Management Information Systems 55
  • Business and International Management 7
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Citations per field
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Citations per year

Countries citing papers authored by Daniel Gálvez

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Gálvez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Gálvez, 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 Daniel Gálvez Line = papers co-authored together Daniel Gálvez links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI
Hit paper breakdown →
2016458
2 201555
3 201650
4 201839
5 202026
6 201326
7 202215
8 201810
9
Multilingual Spoken Words Corpus
20217
10 20206
11 20194
12 20204
13 20154
14 20133
15 20242
16 20222
17 20212
18 20251
19 20231
20 20241

About Daniel Gálvez

Daniel Gálvez is a scholar working on Strategy and Management, Artificial Intelligence, Signal Processing, Management Information Systems and Economics and Econometrics, having authored 23 papers that have together received 718 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (6 papers), Innovation and Knowledge Management (6 papers), Natural Language Processing Techniques (4 papers), Sustainable Supply Chain Management (4 papers), Quality and Supply Management (2 papers), E-Learning and Knowledge Management (2 papers), Music and Audio Processing (2 papers) and Outsourcing and Supply Chain Management (2 papers). The work is most often cited by research in Signal Processing (318 citations), Artificial Intelligence (457 citations), Strategy and Management (136 citations), Management Information Systems (55 citations) and Business and International Management (7 citations). Daniel Gálvez has collaborated with scholars based in France, Chile and United States. Frequent co-authors include Vimal Manohar, Pegah Ghahremani, Yiming Wang, Daniel Povey, Sanjeev Khudanpur, Vijayaditya Peddinti, Xingyu Na, Maurício Camargo, Laure Morel and Michel Fick. Their work appears in journals such as World Patent Information, International Journal of Lean Six Sigma, Process Safety and Environmental Protection, IEEE Access and Technological Forecasting and Social Change.

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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