What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Speech recognition
- Statistics
His scientific interests lie mostly in Speech recognition, Speech enhancement, Speech processing, Reverberation and Signal processing.
His Speech recognition research is multidisciplinary, relying on both Artificial neural network and Microphone array.
His Speech enhancement research includes elements of Noise reduction and Speaker diarisation.
Within one scientific family, Takuya Yoshioka focuses on topics pertaining to Speaker recognition under Speech processing, and may sometimes address concerns connected to Acoustic model and Voice activity detection.
His research integrates issues of Deconvolution and Blind signal separation in his study of Reverberation.
His Word error rate study combines topics in areas such as Feature extraction and Beamforming.
His most cited work include:
- Speech Dereverberation Based on Variance-Normalized Delayed Linear Prediction (214 citations)
- A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research (186 citations)
- Making Machines Understand Us in Reverberant Rooms: Robustness Against Reverberation for Automatic Speech Recognition (182 citations)
What are the main themes of his work throughout his whole career to date?
Takuya Yoshioka mainly focuses on Speech recognition, Speech enhancement, Artificial intelligence, Speech processing and Reverberation.
His Speech recognition research integrates issues from Artificial neural network and Noise.
His Speech enhancement study combines topics from a wide range of disciplines, such as Background noise, Speaker recognition, Audio signal processing, Noise reduction and Linear predictive coding.
The study incorporates disciplines such as Algorithm and Pattern recognition in addition to Artificial intelligence.
His work on Voice activity detection is typically connected to Process as part of general Speech processing study, connecting several disciplines of science.
His Reverberation study integrates concerns from other disciplines, such as Blind signal separation and Signal processing.
He most often published in these fields:
- Speech recognition (83.11%)
- Speech enhancement (27.03%)
- Artificial intelligence (22.97%)
What were the highlights of his more recent work (between 2019-2021)?
- Speech recognition (83.11%)
- Monaural (8.11%)
- End-to-end principle (8.11%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Speech recognition, Monaural, End-to-end principle, Word error rate and Speaker diarisation.
His study looks at the relationship between Speech recognition and fields such as Joint, as well as how they intersect with chemical problems.
Takuya Yoshioka has included themes like Speaker recognition and Data set in his Monaural study.
He usually deals with End-to-end principle and limits it to topics linked to Microphone and Stream processing, Direction of arrival, Contrast, Adaptive beamformer and Robustness.
His studies in Word error rate integrate themes in fields like Recurrent neural network and Transformer.
Speaker diarisation is closely attributed to Speech enhancement in his research.
Between 2019 and 2021, his most popular works were:
- Dual-Path RNN: Efficient Long Sequence Modeling for Time-Domain Single-Channel Speech Separation (53 citations)
- CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings (49 citations)
- Continuous Speech Separation: Dataset and Analysis (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Speech recognition
- Statistics
His primary scientific interests are in Speech recognition, End-to-end principle, Transcription, Monaural and Speaker diarisation.
His Speech recognition research is multidisciplinary, incorporating elements of Speech enhancement and Relevance.
His work deals with themes such as Robustness and Microphone, which intersect with End-to-end principle.
He interconnects Speaker recognition, Speaker identification and Joint in the investigation of issues within Monaural.
His Joint research incorporates themes from Mutual information, Cluster analysis and Joint probability distribution.
His biological study spans a wide range of topics, including Natural and Conversational speech.
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