H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 35 Citations 8,042 204 World Ranking 5884 National Ranking 168

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Speech recognition, Artificial intelligence, Mel-frequency cepstrum, Cognitive load and Pattern recognition are his primary areas of study. Julien Epps studies Voice activity detection, a branch of Speech recognition. Bag-of-words model is closely connected to Machine learning in his research, which is encompassed under the umbrella topic of Artificial intelligence.

His Mel-frequency cepstrum study which covers Feature that intersects with Set, Voice analysis, Relation and Contrast. The concepts of his Pattern recognition study are interwoven with issues in Consensus clustering and Data mining. His research integrates issues of Adjusted mutual information and Cluster analysis in his study of Normalization.

His most cited work include:

  • Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance (1087 citations)
  • The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (528 citations)
  • Information theoretic measures for clusterings comparison: is a correction for chance necessary? (451 citations)

What are the main themes of his work throughout his whole career to date?

Julien Epps mostly deals with Speech recognition, Artificial intelligence, Pattern recognition, Feature extraction and Speaker recognition. His Speech recognition study combines topics from a wide range of disciplines, such as Mixture model, Feature, Emotion classification and Mel-frequency cepstrum. His research in Feature intersects with topics in Depression and Set.

His Artificial intelligence research includes themes of Cognitive load, Machine learning, Computer vision and Natural language processing. His Cognitive load research focuses on subjects like Formant, which are linked to Vocal tract. His studies deal with areas such as Word error rate, Electroencephalography and Signal processing as well as Pattern recognition.

He most often published in these fields:

  • Speech recognition (51.79%)
  • Artificial intelligence (37.86%)
  • Pattern recognition (22.14%)

What were the highlights of his more recent work (between 2018-2021)?

  • Speech recognition (51.79%)
  • Artificial intelligence (37.86%)
  • Feature extraction (13.57%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Speech recognition, Artificial intelligence, Feature extraction, Depression and Feature. His Manner of articulation study, which is part of a larger body of work in Speech recognition, is frequently linked to Spoofing attack, bridging the gap between disciplines. His Manner of articulation research is multidisciplinary, incorporating elements of Bigram, Normalization, Duration and Vocal tract.

His Artificial intelligence research integrates issues from Machine learning, Categorical variable, Computer vision and Pattern recognition. His research investigates the link between Pattern recognition and topics such as Flexibility that cross with problems in Ranking. His Feature research includes elements of Valence and Set.

Between 2018 and 2021, his most popular works were:

  • Direct Modelling of Speech Emotion from Raw Speech (18 citations)
  • Direct modelling of speech emotion from raw speech (15 citations)
  • Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition (12 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary areas of investigation include Speech recognition, Artificial intelligence, Feature extraction, Feature and Depression. His work is dedicated to discovering how Speech recognition, Convolutional neural network are connected with Vocal tract and other disciplines. Julien Epps has researched Artificial intelligence in several fields, including Depression score, Machine learning and Task analysis.

His studies in Machine learning integrate themes in fields like Multi-task learning and Speaker recognition. The various areas that he examines in his Feature extraction study include Artificial neural network, Deep learning, Emotion classification and Filter bank. His study focuses on the intersection of Depression and fields such as Stress with connections in the field of 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.

Top Publications

Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance

Nguyen Xuan Vinh;Julien Epps;James Bailey.
Journal of Machine Learning Research (2010)

1822 Citations

Information theoretic measures for clusterings comparison: is a correction for chance necessary?

Nguyen Xuan Vinh;Julien Epps;James Bailey.
international conference on machine learning (2009)

699 Citations

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Florian Eyben;Klaus R. Scherer;Bjorn W. Schuller;Johan Sundberg.
IEEE Transactions on Affective Computing (2016)

536 Citations

A review of depression and suicide risk assessment using speech analysis

Nicholas Cummins;Stefan Scherer;Jarek Krajewski;Sebastian Schnieder.
Speech Communication (2015)

392 Citations

Signal Processing in Sequence Analysis: Advances in Eukaryotic Gene Prediction

M. Akhtar;J. Epps;E. Ambikairajah.
IEEE Journal of Selected Topics in Signal Processing (2008)

192 Citations

A study of hand shape use in tabletop gesture interaction

Julien Epps;Serge Lichman;Mike Wu.
human factors in computing systems (2006)

153 Citations

The INTERSPEECH 2014 Computational Paralinguistics Challenge: Cognitive & Physical Load

Björn W. Schuller;Stefan Steidl;Anton Batliner;Julien Epps.
conference of the international speech communication association (2014)

148 Citations

An Investigation of Depressed Speech Detection: Features and Normalization.

Nicholas Cummins;Julien Epps;Michael Breakspear;Roland Goecke.
conference of the international speech communication association (2011)

130 Citations

Multimodal assistive technologies for depression diagnosis and monitoring

Jyoti Joshi;Roland Goecke;Roland Goecke;Sharifa Alghowinem;Abhinav Dhall.
Journal on Multimodal User Interfaces (2013)

122 Citations

A new technique for wideband enhancement of coded narrowband speech

J. Epps;W.H. Holmes.
1999 IEEE Workshop on Speech Coding Proceedings. Model, Coders, and Error Criteria (Cat. No.99EX351) (1999)

118 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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