What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Speech recognition
Speech recognition, Artificial intelligence, Speech enhancement, Speech processing and Word error rate are his primary areas of study.
His Speech recognition research incorporates themes from Artificial neural network and Recurrent neural network.
Shinji Watanabe has included themes like Natural language processing and Pattern recognition in his Artificial intelligence study.
His Speech enhancement research is multidisciplinary, incorporating elements of Microphone array, Noise measurement and Speaker diarisation.
When carried out as part of a general Speech processing research project, his work on Acoustic model is frequently linked to work in Open source, therefore connecting diverse disciplines of study.
His Word error rate research is multidisciplinary, incorporating perspectives in Time delay neural network and Machine learning.
His most cited work include:
- Deep clustering: Discriminative embeddings for segmentation and separation (691 citations)
- The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines (429 citations)
- Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks (402 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Speech recognition, Artificial intelligence, End-to-end principle, Pattern recognition and Artificial neural network.
Shinji Watanabe has researched Speech recognition in several fields, including Speech enhancement, Decoding methods and Recurrent neural network.
His Speech enhancement research includes themes of Microphone array and Source separation.
His research in Artificial intelligence intersects with topics in Machine learning and Natural language processing.
The End-to-end principle study which covers Cluster analysis that intersects with Gibbs sampling.
Noise is closely connected to Acoustic model in his research, which is encompassed under the umbrella topic of Pattern recognition.
He most often published in these fields:
- Speech recognition (75.00%)
- Artificial intelligence (39.50%)
- End-to-end principle (24.16%)
What were the highlights of his more recent work (between 2019-2021)?
- Speech recognition (75.00%)
- End-to-end principle (24.16%)
- Speaker diarisation (9.45%)
In recent papers he was focusing on the following fields of study:
Shinji Watanabe focuses on Speech recognition, End-to-end principle, Speaker diarisation, Transformer and Artificial intelligence.
His work on Word error rate as part of general Speech recognition study is frequently connected to Autoregressive model, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His End-to-end principle study integrates concerns from other disciplines, such as Decoding methods, Connectionism and Near and far field.
The study incorporates disciplines such as Voice activity detection, Subtitle, Sequence and Cluster analysis in addition to Speaker diarisation.
His Transformer research integrates issues from Self attention, Recurrent neural network, Computation and Reduction.
The various areas that he examines in his Artificial intelligence study include Pattern recognition and Natural language processing.
Between 2019 and 2021, his most popular works were:
- Espnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit (51 citations)
- CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings (49 citations)
- ESPnet-ST: All-in-One Speech Translation Toolkit (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Speech recognition
Speech recognition, End-to-end principle, Speaker diarisation, Transformer and Artificial intelligence are his primary areas of study.
His Speech recognition study focuses on Source separation in particular.
His study explores the link between End-to-end principle and topics such as Near and far field that cross with problems in Signal reconstruction and Transcription.
His research integrates issues of Artificial neural network, Subtitle, Track and Cluster analysis in his study of Speaker diarisation.
In his study, Monotonic function and Computer engineering is strongly linked to Recurrent neural network, which falls under the umbrella field of Transformer.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Context, Pattern recognition and Natural language processing.
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