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, Artificial intelligence, Hidden Markov model, Pattern recognition and Vocabulary.
His Speech recognition research is multidisciplinary, incorporating elements of Word and Discriminative model.
The various areas that he examines in his Artificial intelligence study include Set, Maximum a posteriori estimation and Natural language processing.
His Hidden Markov model research includes themes of Acoustic model, Adaptation and Markov process, Markov model.
His Pattern recognition research incorporates elements of Estimation theory, Bayesian probability, Bayes' theorem, Bayesian inference and Probabilistic logic.
His studies deal with areas such as Speech translation, Artificial neural network, Deep neural networks, Interface and Maximum likelihood as well as Vocabulary.
His most cited work include:
- Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains (1991 citations)
- A regression approach to speech enhancement based on deep neural networks (737 citations)
- Minimum classification error rate methods for speech recognition (595 citations)
What are the main themes of his work throughout his whole career to date?
Chin-Hui Lee mostly deals with Speech recognition, Artificial intelligence, Pattern recognition, Hidden Markov model and Word error rate.
His Speech recognition research is multidisciplinary, relying on both Artificial neural network, Speech enhancement and Vocabulary.
His biological study spans a wide range of topics, including Machine learning and Natural language processing.
His studies deal with areas such as Prior probability and Set as well as Pattern recognition.
His Hidden Markov model research focuses on Maximum a posteriori estimation and how it connects with Bayesian probability.
His Word error rate research is multidisciplinary, incorporating perspectives in Acoustic model, Decoding methods, Reduction and Test set.
He most often published in these fields:
- Speech recognition (67.48%)
- Artificial intelligence (63.91%)
- Pattern recognition (34.96%)
What were the highlights of his more recent work (between 2014-2021)?
- Speech recognition (67.48%)
- Artificial intelligence (63.91%)
- Speech enhancement (10.71%)
In recent papers he was focusing on the following fields of study:
His main research concerns Speech recognition, Artificial intelligence, Speech enhancement, Artificial neural network and Word error rate.
Many of his studies involve connections with topics such as Noise measurement and Speech recognition.
His Artificial intelligence research is multidisciplinary, relying on both Natural language processing, Machine learning and Pattern recognition.
His work carried out in the field of Pattern recognition brings together such families of science as Channel and Communication channel.
Chin-Hui Lee has researched Speech enhancement in several fields, including Intelligibility, Algorithm and Background noise.
He interconnects Reduction, Decoding methods, Mandarin Chinese, Tone and Test set in the investigation of issues within Word error rate.
Between 2014 and 2021, his most popular works were:
- A regression approach to speech enhancement based on deep neural networks (737 citations)
- Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (136 citations)
- Multiple-target deep learning for LSTM-RNN based speech enhancement (98 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Speech recognition, Artificial intelligence, Artificial neural network, Speech enhancement and Noise measurement.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Natural language processing and Pattern recognition.
His work deals with themes such as Signal-to-noise ratio, Algorithm, Mel-frequency cepstrum and Transfer of learning, which intersect with Artificial neural network.
His work on PESQ as part of his general Speech enhancement study is frequently connected to Interference, thereby bridging the divide between different branches of science.
His Noise measurement research incorporates themes from Recurrent neural network and Noise.
The study incorporates disciplines such as Machine learning, Scalability, Reduction and Coarticulation in addition to Hidden Markov model.
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