Hermann Ney
Impact in
- Artificial Intelligence top 0.01%
- Natural Language Processing Techniques
- Topic Modeling
- Speech Recognition and Synthesis
- Speech and dialogue systems
- Text Readability and Simplification
- Signal Processing top 0.02%
- Speech and Audio Processing
- Music and Audio Processing
Papers in
-
- Natural Language Processing Techniques 440
- Speech Recognition and Synthesis 318
- Topic Modeling 311
- Speech and dialogue systems 111
- Algorithms and Data Compression 77
-
- Speech and Audio Processing 152
- Music and Audio Processing 124
- Co-authors
- Franz Josef Och (23 shared papers)Ralf Schlüter (194 shared papers)Reinhard Kneser (7 shared papers)Martin Sundermeyer (14 shared papers)Oscar Koller (16 shared papers)Christoph Tillmann (10 shared papers)Thomas Deselaers (31 shared papers)Richard Zens (30 shared papers)
- Journals
- Language Resources and Evaluation (15 papers)Speech Communication (12 papers)IEEE Transactions on Audio Speech and Language Processing (11 papers)IEEE Transactions on Speech and Audio Processing (10 papers)IEEE Transactions on Pattern Analysis and Machine Intelligence (9 papers)
- Partner nations
- GermanyFranceUnited States
In The Last Decade
Hermann Ney
712 papers receiving 24.0k citations
Hermann Ney's Hit Papers
Peers
Comparison fields: 5 of 180
- Artificial Intelligence 22.3k
- Signal Processing 5.4k
- Human-Computer Interaction 2.7k
- Computer Vision and Pattern Recognition 6.5k
- Developmental and Educational Psychology 1.7k
Countries citing papers authored by Hermann Ney
This map shows the geographic impact of Hermann Ney'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 Hermann Ney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hermann Ney more than expected).
Fields of papers citing papers by Hermann Ney
This network shows the impact of papers produced by Hermann Ney. 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 Hermann Ney. The network helps show where Hermann Ney may publish in the future.
Co-authors
The 25 scholars most cited alongside Hermann Ney, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 741 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A Systematic Comparison of Various Statistical Alignment Models Hit paper breakdown → | 2003 | 2665 |
| 2 | LSTM neural networks for language modeling Hit paper breakdown → | 2012 | 1226 |
| 3 | Improved backing-off for M-gram language modeling Hit paper breakdown → | 2002 | 927 |
| 4 | Discriminative training and maximum entropy models for statistical machine translation Hit paper breakdown → | 2001 | 734 |
| 5 | Improved statistical alignment models Hit paper breakdown → | 2000 | 706 |
| 6 | The Alignment Template Approach to Statistical Machine Translation Hit paper breakdown → | 2004 | 621 |
| 7 | HMM-based word alignment in statistical translation Hit paper breakdown → | 1996 | 557 |
| 8 | Joint-sequence models for grapheme-to-phoneme conversion Hit paper breakdown → | 2008 | 404 |
| 9 | Neural Sign Language Translation Hit paper breakdown → | 2018 | 402 |
| 10 | 2007 | 381 | |
| 11 | From Feedforward to Recurrent LSTM Neural Networks for Language Modeling Hit paper breakdown → | 2015 | 360 |
| 12 | Improved Alignment Models for Statistical Machine Translation | 1999 | 324 |
| 13 | Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers Hit paper breakdown → | 2015 | 314 |
| 14 | 1994 | 303 | |
| 15 | 2001 | 286 | |
| 16 | 1997 | 273 | |
| 17 | 1992 | 225 | |
| 18 | Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos Hit paper breakdown → | 2019 | 225 |
| 19 | 2004 | 212 | |
| 20 | An Evaluation Tool for Machine Translation: Fast Evaluation for MT Research | 2000 | 194 |
About Hermann Ney
Hermann Ney is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Human-Computer Interaction and Developmental and Educational Psychology, having authored 741 papers that have together received 28.1k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (440 papers), Speech Recognition and Synthesis (318 papers), Topic Modeling (311 papers), Speech and Audio Processing (152 papers), Music and Audio Processing (124 papers), Speech and dialogue systems (111 papers), Algorithms and Data Compression (77 papers) and Handwritten Text Recognition Techniques (48 papers). The work is most often cited by research in Artificial Intelligence (22.3k citations), Signal Processing (5.4k citations), Human-Computer Interaction (2.7k citations), Computer Vision and Pattern Recognition (6.5k citations) and Developmental and Educational Psychology (1.7k citations). Hermann Ney has collaborated with scholars based in Germany, France and United States. Frequent co-authors include Franz Josef Och, Ralf Schlüter, Reinhard Kneser, Martin Sundermeyer, Oscar Koller, Christoph Tillmann, Thomas Deselaers, Richard Zens, M. Bisani and Richard Bowden. Their work appears in journals such as Language Resources and Evaluation, Speech Communication, IEEE Transactions on Audio Speech and Language Processing, IEEE Transactions on Speech and Audio Processing and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.