What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Speech recognition
Lawrence R. Rabiner spends much of his time researching Speech recognition, Artificial intelligence, Algorithm, Pattern recognition and Speech processing.
As part of one scientific family, Lawrence R. Rabiner deals mainly with the area of Speech recognition, narrowing it down to issues related to the Signal, and often Digital signal processing.
His studies in Artificial intelligence integrate themes in fields like Markov process, Markov chain and Natural language processing.
The Algorithm study combines topics in areas such as Pitch detection algorithm, Convolution, Overlap–add method and Bluestein's FFT algorithm.
When carried out as part of a general Speech processing research project, his work on Voice activity detection and Speech analytics is frequently linked to work in Simple, therefore connecting diverse disciplines of study.
His Hidden Markov model study frequently involves adjacent topics like Markov model.
His most cited work include:
- A tutorial on hidden Markov models and selected applications in speech recognition (20443 citations)
- Fundamentals of speech recognition (6607 citations)
- An introduction to hidden Markov models (3817 citations)
What are the main themes of his work throughout his whole career to date?
Lawrence R. Rabiner mainly focuses on Speech recognition, Artificial intelligence, Word, Pattern recognition and Vocabulary.
His research on Speech recognition frequently links to adjacent areas such as Set.
The concepts of his Artificial intelligence study are interwoven with issues in Signal and Natural language processing.
Lawrence R. Rabiner has researched Word in several fields, including Dynamic time warping, Cluster analysis, Algorithm, String and Pattern recognition.
Lawrence R. Rabiner has included themes like Markov process, Markov chain and Markov model in his Hidden Markov model study.
His work carried out in the field of Speech processing brings together such families of science as Digital signal processing and Speech synthesis.
He most often published in these fields:
- Speech recognition (55.81%)
- Artificial intelligence (30.00%)
- Word (21.63%)
What were the highlights of his more recent work (between 1993-2017)?
- Speech recognition (55.81%)
- Artificial intelligence (30.00%)
- Multimedia (3.49%)
In recent papers he was focusing on the following fields of study:
Lawrence R. Rabiner mainly investigates Speech recognition, Artificial intelligence, Multimedia, Speech processing and Speech technology.
His work in Speech analytics, Acoustic model, Dictation, Speaker recognition and Transcription are all subfields of Speech recognition research.
His work deals with themes such as Natural language processing and Pattern recognition, which intersect with Artificial intelligence.
His Speech processing study combines topics from a wide range of disciplines, such as Telecommunications network and Speech synthesis.
Lawrence R. Rabiner focuses mostly in the field of Speech technology, narrowing it down to topics relating to Digital signal processing and, in certain cases, Multidimensional signal processing, Speech enhancement and Acoustics.
His research in Hidden Markov model tackles topics such as Sigmoid function which are related to areas like Outlier and Covariance.
Between 1993 and 2017, his most popular works were:
- Multirate Digital Signal Processing (601 citations)
- Active user registry (255 citations)
- Pots/packet bridge (254 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
The scientist’s investigation covers issues in Speech recognition, Multimedia, Speech technology, Speech processing and Artificial intelligence.
His Speech recognition study integrates concerns from other disciplines, such as Matching and Natural language.
Lawrence R. Rabiner interconnects IP Multimedia Subsystem, User interface, Digital signal processing, Telecommunications and Key in the investigation of issues within Multimedia.
His Digital signal processing research includes elements of Voice activity detection and Signal processing.
His work is dedicated to discovering how Speech processing, Speech synthesis are connected with Wireless network, Information access, Voice command device, Computer telephony integration and Speech coding and other disciplines.
His Artificial intelligence research focuses on subjects like Natural language processing, which are linked to Speech corpus, Utterance, Transcription and TRACE.
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