1980 - IEEE Fellow For contributions to the theory of linear prediction and its applications to spectral estimation, speech analysis, and data compression.
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.
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.
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.
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.
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.
Linear prediction: A tutorial review
Proceedings of the IEEE (1975)
Enhancement of speech corrupted by acoustic noise
M. Berouti;R. Schwartz;J. Makhoul.
international conference on acoustics, speech, and signal processing (1979)
Vector quantization in speech coding
J. Makhoul;S. Roucos;H. Gish.
Proceedings of the IEEE (1985)
PERFORMANCE MEASURES FOR INFORMATION EXTRACTION
John Makhoul;Francis Kubala;Richard Schwartz;Ralph Weischedel.
A compact model for speaker-adaptive training
T. Anastasakos;J. McDonough;R. Schwartz;J. Makhoul.
international conference on spoken language processing (1996)
Fast and Robust Neural Network Joint Models for Statistical Machine Translation
Jacob Devlin;Rabih Zbib;Zhongqiang Huang;Thomas Lamar.
meeting of the association for computational linguistics (2014)
Discrete all-pole modeling
A. El-Jaroudi;J. Makhoul.
IEEE Transactions on Signal Processing (1991)
Stable and efficient lattice methods for linear prediction
IEEE Transactions on Acoustics, Speech, and Signal Processing (1977)
A fast cosine transform in one and two dimensions
IEEE Transactions on Acoustics, Speech, and Signal Processing (1980)
Context-dependent modeling for acoustic-phonetic recognition of continuous speech
R. Schwartz;Y. Chow;O. Kimball;S. Roucos.
international conference on acoustics, speech, and signal processing (1985)
Profile was last updated on December 6th, 2021.
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