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 30 Citations 4,846 216 World Ranking 8663 National Ranking 4050

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Speech recognition

His scientific interests lie mostly in Speech recognition, Artificial intelligence, Speech enhancement, Artificial neural network and Convolutional neural network. His study in the field of Hidden Markov model also crosses realms of Focus. The various areas that Yu Tsao examines in his Artificial intelligence study include Natural language processing, Machine learning and Pattern recognition.

His Speech enhancement study combines topics in areas such as End-to-end principle, Noise measurement, Noise reduction and Spectrogram. His work deals with themes such as Mixture model, Feature extraction, Mel-frequency cepstrum and Support vector machine, which intersect with Artificial neural network. His Convolutional neural network research focuses on Linear programming and how it relates to Signal-to-noise ratio.

His most cited work include:

  • Speech enhancement based on deep denoising autoencoder. (460 citations)
  • End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks (143 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?

Yu Tsao mainly focuses on Speech recognition, Artificial intelligence, Speech enhancement, Pattern recognition and Intelligibility. His research integrates issues of Feature extraction, Noise reduction and Noise in his study of Speech recognition. His Artificial intelligence study frequently links to related topics such as Machine learning.

His PESQ study, which is part of a larger body of work in Speech enhancement, is frequently linked to Noise, bridging the gap between disciplines. His work on Speaker recognition as part of general Pattern recognition research is often related to Maximum a posteriori estimation, thus linking different fields of science. His study explores the link between Intelligibility and topics such as Speech perception that cross with problems in Cochlear implant and Hearing loss.

He most often published in these fields:

  • Speech recognition (60.73%)
  • Artificial intelligence (43.20%)
  • Speech enhancement (31.72%)

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

  • Speech recognition (60.73%)
  • Speech enhancement (31.72%)
  • Artificial intelligence (43.20%)

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

Yu Tsao mostly deals with Speech recognition, Speech enhancement, Artificial intelligence, Deep learning and Intelligibility. Yu Tsao has included themes like Autoencoder, Noise measurement, Noise and Noise reduction in his Speech recognition study. In general Speech enhancement, his work in PESQ is often linked to Noise linking many areas of study.

His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition. His Deep learning research includes themes of Convolution, Reverberation, Joint and Signal processing. His Intelligibility research incorporates themes from Generative adversarial network, Speech modification, Speech quality, Rule-based machine translation and Speech in noise.

Between 2019 and 2021, his most popular works were:

  • ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech (25 citations)
  • Learning With Learned Loss Function: Speech Enhancement With Quality-Net to Improve Perceptual Evaluation of Speech Quality (15 citations)
  • Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in Variational Autoencoder Based Voice Conversion (14 citations)

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

  • Artificial intelligence
  • Machine learning
  • Speech recognition

Yu Tsao spends much of his time researching Speech recognition, Speech enhancement, Artificial intelligence, Deep learning and Noise measurement. He usually deals with Speech recognition and limits it to topics linked to Noise reduction and Waveform. A large part of his Speech enhancement studies is devoted to PESQ.

His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Air quality index, Pollution and Industrial pollution. His research in the fields of Generative adversarial network overlaps with other disciplines such as Noise. His biological study spans a wide range of topics, including Time domain, Decoding methods and Data modeling.

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

Speech enhancement based on deep denoising autoencoder.

Xugang Lu;Yu Tsao;Shigeki Matsuda;Chiori Hori.
conference of the international speech communication association (2013)

730 Citations

A recommendation mechanism for contextualized mobile advertising

Soe-Tsyr Yuan;Y.W. Tsao.
Expert Systems With Applications (2003)

197 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

End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks

Szu-Wei Fu;Tao-Wei Wang;Yu Tsao;Xugang Lu.
IEEE Transactions on Audio, Speech, and Language Processing (2018)

187 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

SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement.

Szu-Wei Fu;Yu Tsao;Xugang Lu.
conference of the international speech communication association (2016)

157 Citations

S1 and S2 Heart Sound Recognition Using Deep Neural Networks

Tien-En Chen;Shih-I Yang;Li-Ting Ho;Kun-Hsi Tsai.
IEEE Transactions on Biomedical Engineering (2017)

127 Citations

Raw waveform-based speech enhancement by fully convolutional networks

Szu-Wei Fu;Yu Tsao;Xugang Lu;Hisashi Kawai.
asia pacific signal and information processing association annual summit and conference (2017)

116 Citations

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

Shih Hau Fang;Yu Tsao;Min Jing Hsiao;Ji Ying Chen.
Journal of Voice (2019)

112 Citations

Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks

Jen-Cheng Hou;Syu-Siang Wang;Ying-Hui Lai;Yu Tsao.
IEEE Transactions on Emerging Topics in Computational Intelligence (2018)

108 Citations

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