What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Speech recognition, Artificial intelligence, Algorithm, Hidden Markov model and Linear prediction.
His Speech recognition research includes elements of Speech enhancement and Spectral density.
His research integrates issues of Pattern recognition and Natural language processing in his study of Artificial intelligence.
His biological study spans a wide range of topics, including Frequency domain and Speech coding.
His Linear prediction research incorporates elements of Weighting, Iterative method, Parametric model and Autocorrelation.
He has researched Quantization in several fields, including Time domain and Linear system.
His most cited work include:
- Linear prediction: A tutorial review (3581 citations)
- Enhancement of speech corrupted by acoustic noise (1104 citations)
- Vector quantization in speech coding (825 citations)
What are the main themes of his work throughout his whole career to date?
John Makhoul mainly investigates Speech recognition, Artificial intelligence, Natural language processing, Hidden Markov model and Word error rate.
John Makhoul has included themes like Vocabulary, Phonetics and Pattern recognition in his Artificial intelligence study.
His work carried out in the field of Natural language processing brings together such families of science as Word and Arabic.
His research in Hidden Markov model intersects with topics in Feature extraction, Context model, Resource management and Markov model.
His study in Quantization is interdisciplinary in nature, drawing from both Linear prediction, Adaptive predictive coding, Control theory and Autocorrelation.
His Linear prediction study deals with Frequency domain intersecting with Algorithm and Filter bank.
He most often published in these fields:
- Speech recognition (61.75%)
- Artificial intelligence (44.81%)
- Natural language processing (27.32%)
What were the highlights of his more recent work (between 2004-2020)?
- Artificial intelligence (44.81%)
- Speech recognition (61.75%)
- Natural language processing (27.32%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Speech recognition, Natural language processing, Machine translation and Artificial neural network.
He frequently studies issues relating to Pattern recognition and Artificial intelligence.
The concepts of his Pattern recognition study are interwoven with issues in System combination, Harmonic mean and Maximization.
The study incorporates disciplines such as Training set and Arabic in addition to Speech recognition.
His Machine translation research incorporates themes from Tagalog, Translation and Rule-based machine translation.
His Artificial neural network research is multidisciplinary, relying on both Somali, Swahili and Statistical model.
Between 2004 and 2020, his most popular works were:
- PERFORMANCE MEASURES FOR INFORMATION EXTRACTION (439 citations)
- Fast and Robust Neural Network Joint Models for Statistical Machine Translation (426 citations)
- Machine Translation of Arabic Dialects (131 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Arabic, Transcription and Natural language processing.
His Speech recognition study incorporates themes from Information extraction and Pattern recognition.
His Pattern recognition study combines topics in areas such as System combination, Normalization, Maximization and Keyword spotting.
He is involved in the study of Artificial intelligence that focuses on Machine translation in particular.
His Machine translation study combines topics from a wide range of disciplines, such as Time delay neural network, Machine learning, Artificial neural network and Joint.
The Arabic study combines topics in areas such as Compound, BLEU, Vocabulary and Training set.
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