What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Speech recognition
Kazuya Takeda mainly focuses on Speech recognition, Artificial intelligence, Natural language processing, Mixture model and Simulation.
His Speech recognition research includes elements of Speech enhancement, Noise reduction and Reverberation.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Relevance, Vocabulary and Computer vision.
Kazuya Takeda has researched Natural language processing in several fields, including Audio-visual speech recognition, Speech corpus, Speech synthesis, Bottleneck and Thesaurus.
Kazuya Takeda interconnects Soft computing, Fuzzy logic, System identification, Adaptive neuro fuzzy inference system and Multilayer perceptron in the investigation of issues within Mixture model.
His Simulation study integrates concerns from other disciplines, such as Brake and Biometrics.
His most cited work include:
- Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification (217 citations)
- An Open Approach to Autonomous Vehicles (203 citations)
- JNAS: Japanese speech corpus for large vocabulary continuous speech recognition research (192 citations)
What are the main themes of his work throughout his whole career to date?
Kazuya Takeda focuses on Speech recognition, Artificial intelligence, Natural language processing, Speech processing and Pattern recognition.
His Speech recognition research is mostly focused on the topic Speech corpus.
His studies deal with areas such as Machine learning and Computer vision as well as Artificial intelligence.
His study in Voice activity detection and Acoustic model is done as part of Speech processing.
His studies in Acoustics integrate themes in fields like Microphone array and Frequency domain.
His research integrates issues of Word recognition and Word error rate in his study of Microphone.
He most often published in these fields:
- Speech recognition (54.10%)
- Artificial intelligence (34.48%)
- Natural language processing (11.24%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (34.48%)
- Speech recognition (54.10%)
- Computer vision (7.05%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Speech recognition, Computer vision, Machine learning and Deep learning.
His Artificial intelligence research incorporates elements of Trajectory and Pattern recognition.
His Speech recognition research is multidisciplinary, incorporating elements of Acoustics and End-to-end principle.
The study incorporates disciplines such as Point, Volume and Visibility in addition to Computer vision.
His Machine learning study combines topics from a wide range of disciplines, such as Classifier, Context, Task and Control.
In his work, Mean opinion score and Transformer is strongly intertwined with Natural language processing, which is a subfield of Speech processing.
Between 2015 and 2021, his most popular works were:
- Speaker-Dependent WaveNet Vocoder. (181 citations)
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies (150 citations)
- An investigation of multi-speaker training for wavenet vocoder (76 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Kazuya Takeda mostly deals with Artificial intelligence, Speech recognition, Deep learning, Pattern recognition and Hidden Markov model.
Kazuya Takeda focuses mostly in the field of Artificial intelligence, narrowing it down to matters related to Computer vision and, in some cases, Image compression and Volume.
His research in the fields of Voice activity detection overlaps with other disciplines such as Thresholding.
The various areas that he examines in his Deep learning study include Feature, Traffic conditions and CLIPS.
His study in the field of Feature extraction is also linked to topics like Modal.
In his study, Semantics and Cluster analysis is inextricably linked to Chunking, which falls within the broad field of Hidden Markov model.
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