What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Speech recognition
Patrick Kenny mostly deals with Speech recognition, Speaker recognition, Pattern recognition, Artificial intelligence and NIST.
His study in the fields of Speaker diarisation and Speaker verification under the domain of Speech recognition overlaps with other disciplines such as Gaussian process and Focus.
His research in Speaker recognition intersects with topics in Algorithm, Covariance matrix, Communication channel and Word error rate.
He interconnects Normalization and Speech processing in the investigation of issues within Pattern recognition.
His Normalization study combines topics in areas such as Linear discriminant analysis and Support vector machine.
His research integrates issues of Covariance, Feature, Mel-frequency cepstrum and Joint factor analysis in his study of NIST.
His most cited work include:
- Front-End Factor Analysis for Speaker Verification (2283 citations)
- Joint Factor Analysis Versus Eigenchannels in Speaker Recognition (582 citations)
- A Study of Interspeaker Variability in Speaker Verification (504 citations)
What are the main themes of his work throughout his whole career to date?
Patrick Kenny mainly investigates Speech recognition, Artificial intelligence, Pattern recognition, Speaker recognition and NIST.
His study in Speech recognition is interdisciplinary in nature, drawing from both Feature extraction and Mel-frequency cepstrum.
His Normalization, Feature, Cluster analysis and Speech processing study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Gaussian process, bridging the gap between disciplines.
The Pattern recognition study combines topics in areas such as Estimator and Covariance.
The concepts of his Speaker recognition study are interwoven with issues in Channel, Classifier, Covariance matrix and Hidden Markov model.
His NIST research is multidisciplinary, incorporating elements of Reduction, Bayes' theorem and Joint factor analysis.
He most often published in these fields:
- Speech recognition (83.33%)
- Artificial intelligence (60.32%)
- Pattern recognition (53.17%)
What were the highlights of his more recent work (between 2013-2019)?
- Speech recognition (83.33%)
- Artificial intelligence (60.32%)
- Speaker recognition (49.21%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Speaker recognition, Pattern recognition and Speaker verification.
His studies deal with areas such as Feature extraction, Mel-frequency cepstrum and Feature as well as Speech recognition.
Patrick Kenny has included themes like Normalization and Hidden Markov model in his Feature extraction study.
His research in Artificial intelligence is mostly concerned with Speech processing.
His Speaker recognition research is multidisciplinary, relying on both NIST, Subspace topology and Deep neural networks.
The various areas that Patrick Kenny examines in his Pattern recognition study include Iterative method, Cluster analysis and Word error rate.
Between 2013 and 2019, his most popular works were:
- Deep Neural Networks for extracting Baum-Welch statistics for Speaker Recognition (167 citations)
- I-vector-based speaker adaptation of deep neural networks for French broadcast audio transcription (106 citations)
- The RedDots Data Collection for Speaker Recognition (97 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Speech recognition, Speaker recognition, Speaker diarisation, Artificial intelligence and Pattern recognition.
His Speech recognition research incorporates elements of Classifier and Feature extraction.
Patrick Kenny focuses mostly in the field of Speaker recognition, narrowing it down to topics relating to Deep neural networks and, in certain cases, Feature vector and Triphone.
His Speaker diarisation study combines topics in areas such as Iterative method, Session and Data collection.
His work on Cluster analysis, Prior information, Cosine Distance and Speech processing as part of general Artificial intelligence study is frequently linked to Mean-shift, bridging the gap between disciplines.
His Pattern recognition research is mostly focused on the topic Diarization error rate.
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