What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Speech recognition
- Statistics
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.
His most cited work include:
- Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis (193 citations)
- Merlin: An Open Source Neural Network Speech Synthesis System (192 citations)
- Objective Distance Measures for Spectral Discontinuities in Concatenative Speech Synthesis (187 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Speech recognition (75.29%)
- Speech synthesis (52.94%)
- Artificial intelligence (43.82%)
What were the highlights of his more recent work (between 2015-2021)?
- Speech recognition (75.29%)
- Speech synthesis (52.94%)
- Artificial intelligence (43.82%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2021, his most popular works were:
- Merlin: An Open Source Neural Network Speech Synthesis System (192 citations)
- Investigating gated recurrent networks for speech synthesis (95 citations)
- From HMMS to DNNS: Where do the improvements come from? (53 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Speech recognition
- Statistics
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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