What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Speech recognition
- Linguistics
Alex Waibel mainly focuses on Speech recognition, Artificial intelligence, Natural language processing, Artificial neural network and Computer vision.
His research on Speech recognition often connects related topics like Vocabulary.
His Artificial intelligence research includes themes of Human–computer interaction and Pattern recognition.
Natural language processing is a component of his Machine translation, Rule-based machine translation, Speech translation, Natural language and Example-based machine translation studies.
His study in the field of Time delay neural network, Connectionism and Phoneme recognition is also linked to topics like Structure.
His Hidden Markov model study combines topics from a wide range of disciplines, such as Loudspeaker, Feature extraction and Markov process.
His most cited work include:
- Phoneme recognition using time-delay neural networks (2088 citations)
- A real-time face tracker (565 citations)
- A time-delay neural network architecture for isolated word recognition (494 citations)
What are the main themes of his work throughout his whole career to date?
Alex Waibel mainly investigates Artificial intelligence, Speech recognition, Natural language processing, Machine translation and Artificial neural network.
Alex Waibel regularly links together related areas like Pattern recognition in his Artificial intelligence studies.
His Speech recognition research incorporates themes from Time delay neural network and Vocabulary.
His German research extends to Natural language processing, which is thematically connected.
Alex Waibel interconnects Sentence and Task in the investigation of issues within Machine translation.
While the research belongs to areas of Rule-based machine translation, Alex Waibel spends his time largely on the problem of Parsing, intersecting his research to questions surrounding Spoken language.
He most often published in these fields:
- Artificial intelligence (66.43%)
- Speech recognition (57.66%)
- Natural language processing (45.26%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (66.43%)
- Speech recognition (57.66%)
- Natural language processing (45.26%)
In recent papers he was focusing on the following fields of study:
Alex Waibel mostly deals with Artificial intelligence, Speech recognition, Natural language processing, Machine translation and Artificial neural network.
As a part of the same scientific study, Alex Waibel usually deals with the Artificial intelligence, concentrating on Pattern recognition and frequently concerns with Word error rate.
His Speech recognition research integrates issues from Speech translation, End-to-end principle, Connectionism, Feature vector and Set.
His study in Natural language processing is interdisciplinary in nature, drawing from both Word and Task.
Alex Waibel has included themes like Training set, Transformer, German, One-shot learning and Sentence in his Machine translation study.
His Artificial neural network research is multidisciplinary, incorporating elements of Natural language understanding, Vocal tract, Speech production, Von Neumann architecture and Bottleneck.
Between 2014 and 2021, his most popular works were:
- Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder (174 citations)
- Very Deep Self-Attention Networks for End-to-End Speech Recognition. (72 citations)
- Self-Attentional Acoustic Models (67 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Linguistics
- Machine learning
His primary areas of investigation include Artificial intelligence, Speech recognition, Natural language processing, Speech translation and Translation.
His research integrates issues of Repertoire and German in his study of Artificial intelligence.
His studies deal with areas such as Artificial neural network, Recurrent neural network, Connectionism, End-to-end principle and Feature vector as well as Speech recognition.
Alex Waibel interconnects Segmentation, Punctuation and Hidden Markov model in the investigation of issues within Natural language processing.
The concepts of his Speech translation study are interwoven with issues in Transcription, Word and Computer-assisted translation.
His Translation research incorporates elements of Presentation, Multimedia, Sentence, Machine learning and Cross lingual.
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