2022 - Research.com Computer Science in Germany Leader Award
2011 - IEEE Fellow For contributions to statistical language modeling, statistical machine translation, and large vocabulary speech recognition
Hermann Ney mainly investigates Artificial intelligence, Speech recognition, Natural language processing, Machine translation and Word error rate. His study brings together the fields of Pattern recognition and Artificial intelligence. His Speech recognition research focuses on Recurrent neural network and how it relates to Time delay neural network.
His Natural language processing research is multidisciplinary, incorporating perspectives in Beam search and Sign language. His biological study spans a wide range of topics, including Machine learning and Phrase. The various areas that Hermann Ney examines in his Word study include Algorithm and Posterior probability.
Hermann Ney mainly investigates Artificial intelligence, Speech recognition, Natural language processing, Machine translation and Pattern recognition. As part of his studies on Artificial intelligence, he frequently links adjacent subjects like Vocabulary. His work in Speech recognition tackles topics such as Handwriting recognition which are related to areas like Handwriting.
His German research extends to the thematically linked field of Natural language processing. His Pattern recognition study incorporates themes from Feature and Computer vision. His research integrates issues of Bigram, Algorithm and Cache language model in his study of Language model.
His primary areas of study are Artificial intelligence, Speech recognition, Machine translation, Natural language processing and Language model. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Vocabulary and Pattern recognition. Hermann Ney studied Speech recognition and Artificial neural network that intersect with Feature vector.
His Natural language processing study which covers Word that intersects with Transcription. His Language model study combines topics in areas such as Algorithm and Transformer. In general Hidden Markov model, his work in Viterbi algorithm is often linked to Frame linking many areas of study.
His primary scientific interests are in Speech recognition, Artificial intelligence, Machine translation, Natural language processing and Language model. His Speech recognition research incorporates elements of End-to-end principle, Training set and Transformer. His studies in Artificial intelligence integrate themes in fields like Machine learning and Pattern recognition.
The Natural language processing study combines topics in areas such as Optimization algorithm, Futures studies and German. His studies deal with areas such as Word and Vocabulary as well as Language model. His Word error rate study combines topics from a wide range of disciplines, such as Acoustic model and Normalization.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A systematic comparison of various statistical alignment models
Franz Josef Och;Hermann Ney.
Computational Linguistics (2003)
Improved backing-off for M-gram language modeling
R. Kneser;H. Ney.
international conference on acoustics, speech, and signal processing (1995)
Discriminative Training and Maximum Entropy Models for Statistical Machine Translation
Franz Josef Och;Hermann Ney.
meeting of the association for computational linguistics (2002)
LSTM Neural Networks for Language Modeling.
Martin Sundermeyer;Ralf Schlüter;Hermann Ney.
conference of the international speech communication association (2012)
Improved statistical alignment models
Franz Josef Och;Hermann Ney.
meeting of the association for computational linguistics (2000)
The Alignment Template Approach to Statistical Machine Translation
Franz Josef Och;Hermann Ney.
Computational Linguistics (2004)
HMM-based word alignment in statistical translation
Stephan Vogel;Hermann Ney;Christoph Tillmann.
international conference on computational linguistics (1996)
Improved Alignment Models for Statistical Machine Translation
Franz Josef Och;Christoph Tillmann;Hermann Ney.
empirical methods in natural language processing (1999)
On structuring probabilistic dependences in stochastic language modelling
Hermann Ney;Ute Essen;Reinhard Kneser.
Computer Speech & Language (1994)
A comparison of alignment models for statistical machine translation
Franz Josef Och;Hermann Ney.
international conference on computational linguistics (2000)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
RWTH Aachen University
Google (United States)
Apple (United States)
Google (United States)
University of Paderborn
Universitat Politècnica de València
University of Graz
Carnegie Mellon University
Universitat Politècnica de València
Amazon (United States)
Yale-NUS College
The Ohio State University
National Taiwan University
University of Córdoba
Paul Ehrlich Institut
National Institutes of Health
University of Pittsburgh
University of Alberta
University of Illinois at Urbana-Champaign
National Agriculture and Food Research Organization
ETH Zurich
University of Chicago
Imperial College London
Purdue University West Lafayette
The Royal Free Hospital
Charité - University Medicine Berlin