What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Speech recognition
- Machine learning
Junichi Yamagishi mostly deals with Speech synthesis, Speech recognition, Hidden Markov model, Artificial intelligence and Natural language processing.
The study incorporates disciplines such as Duration, Spoofing attack, Acoustic model, Speech processing and Waveform in addition to Speech synthesis.
His Speech recognition study combines topics from a wide range of disciplines, such as Feature extraction and Perception.
He combines subjects such as Speaker diarisation, Speaker adaptation, Emotional expression, Sound quality and Signal with his study of Hidden Markov model.
His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Regression analysis, Cluster analysis and Linear regression.
The concepts of his Natural language processing study are interwoven with issues in Speaking style, Relation, Database, Information processing and Robustness.
His most cited work include:
- The HMM-based speech synthesis system (HTS) version 2.0. (321 citations)
- Analysis of Speaker Adaptation Algorithms for HMM-Based Speech Synthesis and a Constrained SMAPLR Adaptation Algorithm (307 citations)
- Spoofing and countermeasures for speaker verification (280 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Speech recognition, Speech synthesis, Artificial intelligence, Hidden Markov model and Natural language processing.
His work is dedicated to discovering how Speech recognition, Waveform are connected with Filter and other disciplines.
His study in the field of Speech corpus also crosses realms of Naturalness.
His Artificial intelligence study incorporates themes from Machine learning, Computer vision and Pattern recognition.
His Hidden Markov model study integrates concerns from other disciplines, such as Emotional expression, Style and Speech processing.
His research in Spoofing attack intersects with topics in Reliability, Anti spoofing, Replay attack and Biometrics.
He most often published in these fields:
- Speech recognition (74.33%)
- Speech synthesis (61.88%)
- Artificial intelligence (41.38%)
What were the highlights of his more recent work (between 2019-2021)?
- Speech recognition (74.33%)
- Speech synthesis (61.88%)
- Artificial intelligence (41.38%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Speech recognition, Speech synthesis, Artificial intelligence, Waveform and Artificial neural network.
The Speech recognition study combines topics in areas such as Similarity, Reverberation and Phone.
Junichi Yamagishi interconnects End-to-end principle and Natural in the investigation of issues within Speech synthesis.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Natural language processing.
His Waveform research incorporates elements of Speech enhancement, Convolution, Filter and Spectrogram.
His Artificial neural network research incorporates themes from Probabilistic logic and Active listening.
Between 2019 and 2021, his most popular works were:
- Neural Source-Filter Waveform Models for Statistical Parametric Speech Synthesis (35 citations)
- ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech (25 citations)
- ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Speech recognition
Junichi Yamagishi spends much of his time researching Speech recognition, Speech synthesis, Artificial intelligence, Task and Similarity.
His Speech recognition research is multidisciplinary, relying on both Waveform, Reverberation and Active listening.
In his study, Use case is inextricably linked to Spoofing attack, which falls within the broad field of Speech synthesis.
As part of the same scientific family, Junichi Yamagishi usually focuses on Artificial intelligence, concentrating on Machine learning and intersecting with Language model and Training set.
His Similarity research integrates issues from Natural and Adaptation.
His work deals with themes such as Feature vector, Hidden Markov model and Pattern recognition, which intersect with Artificial neural network.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
