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 39 Citations 5,897 187 World Ranking 4808 National Ranking 450

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Zhen-Hua Ling mostly deals with Artificial intelligence, Speech recognition, Hidden Markov model, Artificial neural network and Speech synthesis. His Artificial intelligence research incorporates themes from Machine learning, Pattern recognition and Natural language processing. His Speech recognition study frequently links to other fields, such as Rule-based machine translation.

His work carried out in the field of Hidden Markov model brings together such families of science as Voice activity detection, Formant, Selection and Phone. Zhen-Hua Ling interconnects Conditional probability, Natural language understanding and Similarity in the investigation of issues within Artificial neural network. His Speech synthesis research integrates issues from Intelligibility, Feature extraction, Divergence and Speech processing.

His most cited work include:

  • Enhanced LSTM for Natural Language Inference (607 citations)
  • Voice conversion using deep neural networks with layer-wise generative training (175 citations)
  • Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends (164 citations)

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

His primary scientific interests are in Speech recognition, Artificial intelligence, Speech synthesis, Hidden Markov model and Pattern recognition. The Speech recognition study combines topics in areas such as Mixture model, Waveform, Feature and Feature extraction. His Artificial intelligence study incorporates themes from Context, Machine learning and Natural language processing.

His Speech synthesis research incorporates elements of Intelligibility, Representation, Formant, Autoregressive model and Acoustic model. He has included themes like Deep belief network, Parametric statistics and Selection in his Hidden Markov model study. His Pattern recognition study integrates concerns from other disciplines, such as Boltzmann machine, Maximum likelihood and Cluster analysis.

He most often published in these fields:

  • Speech recognition (59.38%)
  • Artificial intelligence (52.34%)
  • Speech synthesis (40.23%)

What were the highlights of his more recent work (between 2019-2021)?

  • Speech recognition (59.38%)
  • Artificial intelligence (52.34%)
  • Artificial neural network (22.27%)

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

Zhen-Hua Ling mostly deals with Speech recognition, Artificial intelligence, Artificial neural network, Waveform and Speech synthesis. Zhen-Hua Ling integrates Speech recognition with Naturalness in his study. His Artificial intelligence study combines topics from a wide range of disciplines, such as Pattern recognition and Natural language processing.

His Artificial neural network research is multidisciplinary, relying on both Encryption, Header, Network packet and Test set. His studies deal with areas such as Quality, Algorithm, Reverberation and Speech enhancement as well as Waveform. His Speech synthesis study combines topics in areas such as Feature extraction, Spoofing attack, Hidden Markov model and Autoregressive model.

Between 2019 and 2021, his most popular works were:

  • Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations (42 citations)
  • ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech (25 citations)
  • Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion. (16 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary scientific interests are in Artificial neural network, Speech recognition, Artificial intelligence, Language model and Waveform. His Artificial neural network research is multidisciplinary, incorporating perspectives in Traffic classification, Encoder, Training set and Audio signal. His primary area of study in Speech recognition is in the field of Utterance.

His research in Artificial intelligence intersects with topics in Context, Selection and Natural language processing. His Waveform research includes themes of Speech synthesis, Algorithm, Parametric statistics and Autoregressive model. His Parametric statistics research incorporates themes from Signal generator, Generative model and Hidden Markov model.

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

Enhanced LSTM for Natural Language Inference

Qian Chen;Xiaodan Zhu;Zhen-Hua Ling;Si Wei.
meeting of the association for computational linguistics (2017)

752 Citations

Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis

J. Yamagishi;T. Nose;H. Zen;Zhen-Hua Ling.
IEEE Transactions on Audio, Speech, and Language Processing (2009)

232 Citations

Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends

Zhen-Hua Ling;Shi-Yin Kang;Heiga Zen;Andrew Senior.
IEEE Signal Processing Magazine (2015)

231 Citations

Voice conversion using deep neural networks with layer-wise generative training

Ling-Hui Chen;Zhen-Hua Ling;Li-Juan Liu;Li-Rong Dai.
IEEE Transactions on Audio, Speech, and Language Processing (2014)

223 Citations

Neural Natural Language Inference Models Enhanced with External Knowledge

Qian Chen;Xiaodan Zhu;Zhen-Hua Ling;Diana Inkpen.
meeting of the association for computational linguistics (2018)

171 Citations

Modeling Spectral Envelopes Using Restricted Boltzmann Machines and Deep Belief Networks for Statistical Parametric Speech Synthesis

Zhen-Hua Ling;Li Deng;Dong Yu.
IEEE Transactions on Audio, Speech, and Language Processing (2013)

168 Citations

Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints

Quan Liu;Hui Jiang;Si Wei;Zhen-Hua Ling.
international joint conference on natural language processing (2015)

144 Citations

USTC System for Blizzard Challenge 2006 an Improved HMM-based Speech Synthesis Method

Zhen-Hua Ling;Yi-Jian Wu;Yu-Ping Wang;Long Qin.
(2006)

143 Citations

The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods

Jaime Lorenzo-Trueba;Junichi Yamagishi;Tomoki Toda;Daisuke Saito.
Odyssey 2018 The Speaker and Language Recognition Workshop (2018)

143 Citations

Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference.

Qian Chen;Xiaodan Zhu;Zhen-Hua Ling;Si Wei.
arXiv: Computation and Language (2016)

126 Citations

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Best Scientists Citing Zhen-Hua Ling

Junichi Yamagishi

Junichi Yamagishi

National Institute of Informatics

Publications: 127

Tomoki Toda

Tomoki Toda

Nagoya University

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Haizhou Li

Haizhou Li

Chinese University of Hong Kong, Shenzhen

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Simon King

Simon King

University of Edinburgh

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Hirokazu Kameoka

Hirokazu Kameoka

NTT (Japan)

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Nicholas Evans

Nicholas Evans

EURECOM

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Frank K. Soong

Frank K. Soong

Microsoft (United States)

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Keiichi Tokuda

Keiichi Tokuda

Nagoya Institute of Technology

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Mohit Bansal

Mohit Bansal

University of North Carolina at Chapel Hill

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Helen Meng

Helen Meng

Chinese University of Hong Kong

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Yu Tsao

Yu Tsao

Center for Information Technology

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Hsin-Min Wang

Hsin-Min Wang

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Tomi Kinnunen

Tomi Kinnunen

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Heiga Zen

Heiga Zen

Google (United States)

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Hai Zhao

Hai Zhao

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Noah A. Smith

Noah A. Smith

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