H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 31 Citations 5,272 273 World Ranking 7956 National Ranking 73

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Hsin-Min Wang mainly focuses on Speech recognition, Artificial intelligence, Natural language processing, Mandarin Chinese and Syllable. His study in Speech recognition is interdisciplinary in nature, drawing from both Mixture model, Vocabulary and Generative model. His studies in Artificial intelligence integrate themes in fields like Decoding methods, Cable television and Pattern recognition.

His studies deal with areas such as Document retrieval, Search engine indexing and Hidden Markov model as well as Natural language processing. Hsin-Min Wang interconnects The Internet, Natural language and Speech processing in the investigation of issues within Mandarin Chinese. His Syllable study incorporates themes from Speech corpus and Chinese language.

His most cited work include:

  • A DISTRIBUTED ARCHITECTURE FOR COOPERATIVE SPOKEN DIALOGUE AGENTS WITH COHERENT DIALOGUE STATE AND HISTORY (237 citations)
  • Golden Mandarin(II)-an intelligent Mandarin dictation machine for Chinese character input with adaptation/learning functions (161 citations)
  • Voice conversion from non-parallel corpora using variational auto-encoder (139 citations)

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

His primary areas of investigation include Artificial intelligence, Speech recognition, Natural language processing, Pattern recognition and Mandarin Chinese. His work in Artificial intelligence tackles topics such as Machine learning which are related to areas like Training set. The concepts of his Speech recognition study are interwoven with issues in Speech enhancement and Vocabulary.

The Natural language processing study combines topics in areas such as Context and Information retrieval, Relevance, Search engine indexing. His study looks at the relationship between Pattern recognition and topics such as Cluster analysis, which overlap with Bayesian information criterion. His Syllable research incorporates elements of Natural language and Chinese language.

He most often published in these fields:

  • Artificial intelligence (62.08%)
  • Speech recognition (55.90%)
  • Natural language processing (31.46%)

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

  • Speech recognition (55.90%)
  • Artificial intelligence (62.08%)
  • Speech enhancement (11.24%)

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

His primary scientific interests are in Speech recognition, Artificial intelligence, Speech enhancement, Deep learning and Autoencoder. His Speech recognition research is mostly focused on the topic Language model. Hsin-Min Wang interconnects Acoustic model and Mandarin Chinese in the investigation of issues within Language model.

In the field of Artificial intelligence, his study on Training set overlaps with subjects such as Naturalness. His Speech enhancement research is multidisciplinary, relying on both Intelligibility, Noise measurement, Noise reduction and Audio visual. In his research, Classifier and Similarity is intimately related to Code, which falls under the overarching field of Autoencoder.

Between 2018 and 2021, his most popular works were:

  • ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech (25 citations)
  • MOSNet: Deep Learning-Based Objective Assessment for Voice Conversion. (24 citations)
  • Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion (17 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His scientific interests lie mostly in Speech recognition, Artificial intelligence, Speech enhancement, Deep learning and Autoencoder. As a member of one scientific family, Hsin-Min Wang mostly works in the field of Speech recognition, focusing on Artificial neural network and, on occasion, Signal-to-noise ratio, Reinforcement learning, Language model, Word error rate and Mandarin Chinese. His work in the fields of Artificial intelligence, such as Generative model, overlaps with other areas such as Naturalness.

His work carried out in the field of Speech enhancement brings together such families of science as Encoder, Noise reduction and Model selection. His Autoencoder research is multidisciplinary, incorporating elements of Transfer of learning, Code and Classifier, Pattern recognition. In the field of Pattern recognition, his study on Training set overlaps with subjects such as Similarity, Test data, Structure and Frame.

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

A DISTRIBUTED ARCHITECTURE FOR COOPERATIVE SPOKEN DIALOGUE AGENTS WITH COHERENT DIALOGUE STATE AND HISTORY

Bor-shen Lin;Hsin-min Wang;Lin-Shan Lee.
(2000)

247 Citations

Fluent speech prosody: Framework and modeling

Chiu-yu Tseng;Shao-huang Pin;Yehlin Lee;Hsin-min Wang.
Speech Communication (2005)

207 Citations

Voice conversion from non-parallel corpora using variational auto-encoder

Chin-Cheng Hsu;Hsin-Te Hwang;Yi-Chiao Wu;Yu Tsao.
asia pacific signal and information processing association annual summit and conference (2016)

195 Citations

An initial study on large-vocabulary continuous Mandarin speech recognition with limited training data based on sub-syllabic models

Hsin-min Wang;Renyuan Lyu;Jia-lin Shen;Lin-shan Lee.
Int. Computer Symposium (Hsin-chu, R.O.C) (1994)

167 Citations

Golden Mandarin(II)-an intelligent Mandarin dictation machine for Chinese character input with adaptation/learning functions

Lin-Shan Lee;Keh-Jiann Chen;Chiu-Yu Tseng;Renyuan Lyu.
international conference on speech image processing and neural networks (1994)

161 Citations

Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks

Chin-Cheng Hsu;Hsin-Te Hwang;Yi-Chiao Wu;Yu Tsao.
arXiv: Computation and Language (2017)

160 Citations

MATBN: A Mandarin Chinese Broadcast News Corpus

Hsin-Min Wang;Berlin Chen;Jen-Wei Kuo;Shih-Sian Cheng.
International Journal of Computational Linguistics & Chinese Language Processing, Volume 10, Number 2, June 2005: Special Issue on Annotated Speech Corpora (2005)

146 Citations

Fast speaker adaptation using eigenspace-based maximum likelihood linear regression.

Kuan-Ting Chen;Wen-Wei Liau;Hsin-Min Wang;Lin-Shan Lee.
conference of the international speech communication association (2000)

144 Citations

Automatic singer recognition of popular music recordings via estimation and modeling of solo vocal signals

Wei-Ho Tsai;Hsin-Min Wang.
IEEE Transactions on Audio, Speech, and Language Processing (2006)

140 Citations

Complete recognition of continuous Mandarin speech for Chinese language with very large vocabulary using limited training data

Hsin-Min Wang;Tai-Hsuan Ho;Rung-Chiung Yang;Jia-Lin Shen.
IEEE Transactions on Speech and Audio Processing (1997)

134 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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