What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- 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.
His most cited work include:
- A systematic comparison of various statistical alignment models (3602 citations)
- Improved backing-off for M-gram language modeling (1301 citations)
- LSTM Neural Networks for Language Modeling. (1090 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (71.34%)
- Speech recognition (51.13%)
- Natural language processing (42.16%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (71.34%)
- Speech recognition (51.13%)
- Machine translation (26.91%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- Improved Training of End-to-end Attention Models for Speech Recognition (193 citations)
- Neural Sign Language Translation (116 citations)
- A comprehensive study of deep bidirectional LSTM RNNS for acoustic modeling in speech recognition (106 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
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.
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