H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 55 Citations 12,591 273 World Ranking 2230 National Ranking 1188

Research.com Recognitions

Awards & Achievements

2017 - IEEE Fellow For contributions to speech recognition

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Speech recognition

His primary scientific interests are in Artificial intelligence, Speech recognition, Pattern recognition, Feature extraction and Speech processing. Bhiksha Raj has included themes like Machine learning and Non-negative matrix factorization in his Artificial intelligence study. He interconnects Speech enhancement, Vocabulary and Robustness in the investigation of issues within Speech recognition.

His work carried out in the field of Pattern recognition brings together such families of science as Feature, Spectrogram, Source separation, Latent variable model and Noise reduction. In his study, Voice search, Missing data, Beamforming and Array processing is strongly linked to Voice activity detection, which falls under the umbrella field of Feature extraction. His Speech processing research includes elements of Microphone array processing and Word error rate.

His most cited work include:

  • SphereFace: Deep Hypersphere Embedding for Face Recognition (1077 citations)
  • A vector Taylor series approach for environment-independent speech recognition (418 citations)
  • Sphinx-4: a flexible open source framework for speech recognition (366 citations)

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

His primary areas of investigation include Speech recognition, Artificial intelligence, Pattern recognition, Machine learning and Speech processing. His research on Speech recognition frequently connects to adjacent areas such as Noise. His studies link Natural language processing with Artificial intelligence.

His Pattern recognition study incorporates themes from Feature, Signal, Feature and Non-negative matrix factorization. His research on Machine learning focuses in particular on Supervised learning. His research combines Speech enhancement and Speech processing.

He most often published in these fields:

  • Speech recognition (48.03%)
  • Artificial intelligence (46.35%)
  • Pattern recognition (26.40%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (46.35%)
  • Speech recognition (48.03%)
  • Artificial neural network (6.74%)

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

Bhiksha Raj focuses on Artificial intelligence, Speech recognition, Artificial neural network, Machine learning and Pattern recognition. His studies in Artificial intelligence integrate themes in fields like Communication channel and Natural language processing. His specific area of interest is Speech recognition, where Bhiksha Raj studies Spectrogram.

His Artificial neural network research is multidisciplinary, incorporating elements of Algorithm, Metadata and Hidden Markov model. His study in the fields of Softmax function and Discriminative model under the domain of Pattern recognition overlaps with other disciplines such as Laplace operator. His Convolutional neural network study deals with Feature intersecting with Feature extraction.

Between 2016 and 2021, his most popular works were:

  • SphereFace: Deep Hypersphere Embedding for Face Recognition (1077 citations)
  • SphereFace: Deep Hypersphere Embedding for Face Recognition (228 citations)
  • DCASE 2017 Challenge setup: Tasks, datasets and baseline system (168 citations)

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

SphereFace: Deep Hypersphere Embedding for Face Recognition

Weiyang Liu;Yandong Wen;Zhiding Yu;Ming Li.
computer vision and pattern recognition (2017)

780 Citations

A vector Taylor series approach for environment-independent speech recognition

P.J. Moreno;B. Raj;R.M. Stern.
international conference on acoustics speech and signal processing (1996)

606 Citations

Sphinx-4: a flexible open source framework for speech recognition

Willie Walker;Paul Lamere;Philip Kwok;Bhiksha Raj.
(2004)

601 Citations

DCASE 2017 Challenge setup: Tasks, datasets and baseline system

Annamaria Mesaros;Toni Heittola;Aleksandr Diment;Benjamin Elizalde.
DCASE 2017 - Workshop on Detection and Classification of Acoustic Scenes and Events (2017)

389 Citations

Speech denoising using nonnegative matrix factorization with priors

K.W. Wilson;B. Raj;P. Smaragdis;A. Divakaran.
international conference on acoustics, speech, and signal processing (2008)

314 Citations

Supervised and semi-supervised separation of sounds from single-channel mixtures

Paris Smaragdis;Bhiksha Raj;Madhusudana Shashanka.
international conference on independent component analysis and signal separation (2007)

300 Citations

Missing-feature approaches in speech recognition

B. Raj;R.M. Stern.
IEEE Signal Processing Magazine (2005)

284 Citations

Reconstruction of missing features for robust speech recognition

Bhiksha Raj;Michael L. Seltzer;Richard M. Stern.
Speech Communication (2004)

278 Citations

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

Keisuke Kinoshita;Marc Delcroix;Sharon Gannot;Emanuël A. P. Habets.
EURASIP Journal on Advances in Signal Processing (2016)

262 Citations

Beyond Gaussian Pyramid: Multi-skip Feature Stacking for action recognition

Zhenzhong Lan;Ming Lin;Xuanchong Li;Alexander G. Hauptmann.
computer vision and pattern recognition (2015)

234 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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