What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Linguistics
- Speech recognition
His primary areas of investigation include Artificial intelligence, Speech recognition, Natural language processing, Pattern recognition and Language model.
Tatsuya Kawahara combines subjects such as Decoding methods and Language acquisition with his study of Artificial intelligence.
His studies deal with areas such as Sentence and Vocabulary as well as Speech recognition.
The Phrase, Machine translation, Parsing and Rule-based machine translation research Tatsuya Kawahara does as part of his general Natural language processing study is frequently linked to other disciplines of science, such as Search terms, therefore creating a link between diverse domains of science.
His study in the fields of Mixture model under the domain of Pattern recognition overlaps with other disciplines such as Non-negative matrix factorization.
Tatsuya Kawahara has researched Language model in several fields, including Transcription, Pronunciation and Natural language.
His most cited work include:
- Julius --- An Open Source Real-Time Large Vocabulary Recognition Engine (437 citations)
- Recent Development of Open-Source Speech Recognition Engine Julius (235 citations)
- Free software toolkit for Japanese large vocabulary continuous speech recognition (87 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Speech recognition, Artificial intelligence, Natural language processing, Language model and Pattern recognition.
His Speech recognition study combines topics from a wide range of disciplines, such as Sentence and Word.
Artificial intelligence is closely attributed to Vocabulary in his research.
His study looks at the relationship between Natural language processing and fields such as Search engine indexing, as well as how they intersect with chemical problems.
The concepts of his Language model study are interwoven with issues in Cache language model, Adaptation and Lexicon.
His Acoustic model study frequently draws connections between adjacent fields such as Speech corpus.
He most often published in these fields:
- Speech recognition (64.54%)
- Artificial intelligence (56.90%)
- Natural language processing (42.04%)
What were the highlights of his more recent work (between 2017-2021)?
- Speech recognition (64.54%)
- Artificial intelligence (56.90%)
- End-to-end principle (3.18%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Speech recognition, Artificial intelligence, End-to-end principle, Language model and Human–computer interaction.
His Speech recognition research includes themes of Encoder, Word, Inference, Decoding methods and Speech enhancement.
The Artificial intelligence study combines topics in areas such as Machine learning and Natural language processing.
His Natural language processing research is multidisciplinary, incorporating elements of Transfer of learning and Relevance.
His Language model study combines topics in areas such as Vocabulary, Leverage, Hybrid system and Hidden Markov model.
His Human–computer interaction research also works with subjects such as
- Human–robot interaction together with Conversation and Humanoid robot,
- Android, which have a strong connection to World Wide Web,
- Active listening which intersects with area such as Process.
Between 2017 and 2021, his most popular works were:
- Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization (60 citations)
- Multilingual End-to-End Speech Translation (29 citations)
- Bayesian Multichannel Speech Enhancement with a Deep Speech Prior (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Linguistics
- Speech recognition
His main research concerns Speech recognition, Artificial intelligence, Non-negative matrix factorization, Speech enhancement and Natural language processing.
His Speech recognition study incorporates themes from Decoding methods, Word and Transformer.
His works in Deep learning and Bayesian inference are all subjects of inquiry into Artificial intelligence.
His Speech enhancement research focuses on subjects like Noise measurement, which are linked to Probabilistic logic and Generative model.
His Natural language processing research includes elements of Transfer of learning and End-to-end principle.
His research integrates issues of Training set, Hybrid system and Speech synthesis in his study of Language model.
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