What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Speech recognition
- Statistics
Mark A. Clements spends much of his time researching Speech recognition, Speech processing, Artificial intelligence, Stress and Waveform.
Linear predictive coding is the focus of his Speech recognition research.
His studies in Speech processing integrate themes in fields like Feature, Spoken language, Formant, Adaptation and Speech synthesis.
The study incorporates disciplines such as Spelling, Vocabulary and Natural language processing in addition to Artificial intelligence.
Mark A. Clements combines subjects such as Feature extraction and Vocal tract with his study of Stress.
Mark A. Clements interconnects Electroglottograph, Acoustic wave, Signal processing, Algorithm and Glottal closure in the investigation of issues within Waveform.
His most cited work include:
- Constrained iterative speech enhancement with application to speech recognition (224 citations)
- Singing voice synthesis (184 citations)
- The challenge of spoken language systems: Research directions for the nineties (147 citations)
What are the main themes of his work throughout his whole career to date?
Mark A. Clements mostly deals with Speech recognition, Artificial intelligence, Speech processing, Pattern recognition and Hidden Markov model.
His research in Speech recognition intersects with topics in Speech enhancement and Noise.
His Artificial intelligence research incorporates themes from Machine learning and Natural language processing.
His study in Speech processing is interdisciplinary in nature, drawing from both Vocal tract, Waveform, Speech production, Speech synthesis and Algorithm.
His work carried out in the field of Pattern recognition brings together such families of science as Spike sorting and Cluster analysis.
In the subject of general Hidden Markov model, his work in Viterbi algorithm is often linked to Measure, thereby combining diverse domains of study.
He most often published in these fields:
- Speech recognition (69.28%)
- Artificial intelligence (37.95%)
- Speech processing (28.92%)
What were the highlights of his more recent work (between 2010-2019)?
- Artificial intelligence (37.95%)
- Pattern recognition (27.71%)
- Speech recognition (69.28%)
In recent papers he was focusing on the following fields of study:
Mark A. Clements spends much of his time researching Artificial intelligence, Pattern recognition, Speech recognition, Laughter and Autism spectrum disorder.
The Hidden Markov model research Mark A. Clements does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Adaptive neuro fuzzy inference system, therefore creating a link between diverse domains of science.
His Pattern recognition research incorporates elements of Spike sorting and Word error rate.
Speech recognition is a component of his Formant and Speech processing studies.
His Laughter study combines topics from a wide range of disciplines, such as Paralanguage, Cognitive psychology, Affect and Audiology.
His Autism spectrum disorder research includes elements of Clinical psychology, Standardized test, School age child and Voice activity detection.
Between 2010 and 2019, his most popular works were:
- Decoding Children's Social Behavior (121 citations)
- Automated video-based assessment of surgical skills for training and evaluation in medical schools. (44 citations)
- Naturalistic Language Recordings Reveal "Hypervocal" Infants at High Familial Risk for Autism. (35 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Multimedia, Pattern recognition, Surgical skills and Machine learning.
His Artificial intelligence research is multidisciplinary, relying on both Percentage point and Key.
The concepts of his Multimedia study are interwoven with issues in Audio signal processing, Feature modeling and Video based.
His research integrates issues of Speech recognition and Hybrid system in his study of Pattern recognition.
He studies Formant, a branch of Speech recognition.
His Machine learning research integrates issues from Surgical training and Frequency analysis.
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