D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 43 Citations 7,873 185 World Ranking 3950 National Ranking 2038

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Speech recognition

Jinyu Li spends much of his time researching Speech recognition, Artificial neural network, Artificial intelligence, Word error rate and Hidden Markov model. His specific area of interest is Speech recognition, where Jinyu Li studies Acoustic model. His Artificial neural network study combines topics in areas such as Triphone, Feature extraction, Divergence and Singular value decomposition.

Jinyu Li has researched Artificial intelligence in several fields, including Natural language processing and Pattern recognition. His research integrates issues of Training set and Reduction in his study of Word error rate. His Hidden Markov model research is multidisciplinary, incorporating perspectives in Time delay neural network, Classifier, Classifier and Softmax function.

His most cited work include:

  • Recent advances in deep learning for speech research at Microsoft (533 citations)
  • Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers (437 citations)
  • Restructuring of Deep Neural Network Acoustic Models with Singular Value Decomposition (345 citations)

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

Jinyu Li focuses on Speech recognition, Artificial intelligence, Word error rate, Artificial neural network and Pattern recognition. Jinyu Li interconnects End-to-end principle, Recurrent neural network and Reduction in the investigation of issues within Speech recognition. Jinyu Li usually deals with Artificial intelligence and limits it to topics linked to Machine learning and Probabilistic logic and Estimation theory.

His studies deal with areas such as Language model, Training set, Constraint and Task as well as Word error rate. His work carried out in the field of Artificial neural network brings together such families of science as Feature extraction, Feature, Singular value decomposition and Adaptation. His work on Speaker recognition, Feature vector and Classifier as part of his general Pattern recognition study is frequently connected to Set, thereby bridging the divide between different branches of science.

He most often published in these fields:

  • Speech recognition (81.82%)
  • Artificial intelligence (38.10%)
  • Word error rate (32.90%)

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

  • Speech recognition (81.82%)
  • Word error rate (32.90%)
  • End-to-end principle (12.12%)

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

Jinyu Li mainly focuses on Speech recognition, Word error rate, End-to-end principle, Recurrent neural network and Encoder. Jinyu Li is interested in Acoustic model, which is a field of Speech recognition. His work in Acoustic model covers topics such as Discriminative model which are related to areas like Cross entropy.

In Word error rate, he works on issues like Dictation, which are connected to Regularization. His research in Recurrent neural network intersects with topics in Language model, Latency, Training set and Joint. The Artificial neural network study combines topics in areas such as Stress and Hidden Markov model.

Between 2018 and 2021, his most popular works were:

  • Improving RNN Transducer Modeling for End-to-End Speech Recognition (52 citations)
  • Continuous Speech Separation: Dataset and Analysis (40 citations)
  • Adversarial Speaker Verification (35 citations)

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

  • Artificial intelligence
  • Machine learning
  • Speech recognition

His main research concerns Speech recognition, Word error rate, End-to-end principle, Encoder and Reduction. Many of his research projects under Speech recognition are closely connected to Set with Set, tying the diverse disciplines of science together. His Word error rate research incorporates elements of Conversational speech, Adaptation, Dictation and Speaker adaptation.

The various areas that Jinyu Li examines in his Encoder study include Embedding, Decoding methods and Voice search. His Reduction research includes elements of Language model, Connectionism and Constraint. His Artificial neural network study integrates concerns from other disciplines, such as Microphone array and Speech enhancement.

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.

Best Publications

Recent advances in deep learning for speech research at Microsoft

Li Deng;Jinyu Li;Jui-Ting Huang;Kaisheng Yao.
international conference on acoustics, speech, and signal processing (2013)

666 Citations

Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers

Jui-Ting Huang;Jinyu Li;Dong Yu;Li Deng.
international conference on acoustics, speech, and signal processing (2013)

513 Citations

An overview of noise-robust automatic speech recognition

Jinyu Li;Li Deng;Yifan Gong;Reinhold Haeb-Umbach.
IEEE Transactions on Audio, Speech, and Language Processing (2014)

411 Citations

Restructuring of Deep Neural Network Acoustic Models with Singular Value Decomposition

Jian Xue;Jinyu Li;Yifan Gong.
conference of the international speech communication association (2013)

374 Citations

Learning small-size DNN with output-distribution-based criteria.

Jinyu Li;Rui Zhao;Jui-Ting Huang;Yifan Gong.
conference of the international speech communication association (2014)

250 Citations

Singular value decomposition based low-footprint speaker adaptation and personalization for deep neural network

Jian Xue;Jinyu Li;Dong Yu;Mike Seltzer.
international conference on acoustics, speech, and signal processing (2014)

171 Citations

End-to-End attention based text-dependent speaker verification

Shi-Xiong Zhang;Zhuo Chen;Yong Zhao;Jinyu Li.
spoken language technology workshop (2016)

150 Citations

High-performance hmm adaptation with joint compensation of additive and convolutive distortions via Vector Taylor Series

Jinyu Li;Li Deng;Dong Yu;Yifan Gong.
ieee automatic speech recognition and understanding workshop (2007)

144 Citations

Restructuring deep neural network acoustic models

Jian Xue;Emilian Stoimenov;Jinyu Li;Yifan Gong.
(2013)

118 Citations

Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks.

Dong Yu;Michael L. Seltzer;Jinyu Li;Jui-Ting Huang.
arXiv: Learning (2013)

118 Citations

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