His primary scientific interests are in Speech recognition, Speech synthesis, Artificial intelligence, Natural language processing and Hidden Markov model. His Speech recognition research incorporates themes from Artificial neural network and Context. The Speech synthesis study which covers Speaker recognition that intersects with Spoofing attack.
The various areas that he examines in his Artificial intelligence study include Perception, Simulation, Phone, Machine learning and Pattern recognition. Simon King combines subjects such as Pronunciation, Database, Range, Speech corpus and Text to speech synthesis with his study of Natural language processing. His Hidden Markov model research includes elements of Sound quality, Feature extraction, Robustness and Decision tree.
Simon King mainly investigates Speech recognition, Speech synthesis, Artificial intelligence, Natural language processing and Hidden Markov model. His studies link Artificial neural network with Speech recognition. His studies deal with areas such as Intelligibility, Parametric statistics, Perception and Voice activity detection as well as Speech synthesis.
The concepts of his Artificial intelligence study are interwoven with issues in Context and Pattern recognition. His research in Natural language processing intersects with topics in Pronunciation and Word. His Hidden Markov model research is multidisciplinary, relying on both Feature extraction and Adaptation.
Simon King mostly deals with Speech recognition, Speech synthesis, Artificial intelligence, Artificial neural network and Parametric statistics. His Speech recognition research integrates issues from Variation and Waveform. His work carried out in the field of Speech synthesis brings together such families of science as Statistical model and Hidden Markov model.
His Hidden Markov model research includes themes of Decision tree, Feature extraction, Speaker recognition and Parametric model. His Artificial intelligence research incorporates elements of Machine learning, Pattern recognition, State and Natural language processing. In the field of Artificial neural network, his study on Recurrent neural network overlaps with subjects such as Merlin, Open source, Noise and Mean opinion score.
His main research concerns Speech recognition, Speech synthesis, Artificial intelligence, Artificial neural network and Parametric statistics. His Speech recognition study combines topics from a wide range of disciplines, such as Control and Spoofing attack. His studies in Speech synthesis integrate themes in fields like Intonation, Prosody, Statistical model and Hidden Markov model.
His study looks at the intersection of Hidden Markov model and topics like Feature extraction with Representation, Speaker recognition and Computer security. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Markov model. His research investigates the connection between Artificial neural network and topics such as Pattern recognition that intersect with issues in Parametric model and Hybrid system.
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Merlin: An Open Source Neural Network Speech Synthesis System
Zhizheng Wu;Oliver Watts;Simon King.
9th ISCA Speech Synthesis Workshop (2016)
The Blizzard Challenge 2008
Simon King;Robert A J Clark;Catherine Mayo;Vasilis Karaiskos.
Objective Distance Measures for Spectral Discontinuities in Concatenative Speech Synthesis
Jithendra Vepa;Simon King;Paul Taylor.
conference of the international speech communication association (2002)
Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis
Zhizheng Wu;Cassia Valentini-Botinhao;Oliver Watts;Simon King.
international conference on acoustics, speech, and signal processing (2015)
Detection of phonological features in continuous speech using neural networks
Simon King;Paul Taylor.
Computer Speech & Language (2000)
Speech production knowledge in automatic speech recognition.
Simon King;Joe Frankel;Karen Livescu;Erik McDermott.
Journal of the Acoustical Society of America (2007)
Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis
J. Yamagishi;T. Nose;H. Zen;Zhen-Hua Ling.
IEEE Transactions on Audio, Speech, and Language Processing (2009)
Multisyn: Open-domain unit selection for the Festival speech synthesis system
Robert A. J. Clark;Korin Richmond;Simon King.
Speech Communication (2007)
A study of speaker adaptation for DNN-based speech synthesis
Zhizheng Wu;Pawel Swietojanski;Christophe Veaux;Stephen Renals.
conference of the international speech communication association (2015)
Articulatory Feature-Based Methods for Acoustic and Audio-Visual Speech Recognition: Summary from the 2006 JHU Summer workshop
K. Livescu;O. Cetin;M. Hasegawa-Johnson;S. King.
international conference on acoustics, speech, and signal processing (2007)
Profile was last updated on December 6th, 2021.
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