Zobrazeno 1 - 10 of 27 pro vyhledávání: '"Zhehuai Chen"'
2
Modular End-to-End Automatic Speech Recognition Framework for Acoustic-to-Word Model
Autor: Kai Yu, Mingkun Huang, Yizhou Lu, Hao Li, Qi Liu, Zhehuai Chen
Publikováno v: IEEE/ACM Transactions on Audio, Speech, and Language Processing. 28:2174-2183
End-to-end (E2E) systems have played a more and more important role in automatic speech recognition (ASR) and achieved great performance. However, E2E systems recognize output word sequences directly with the input acoustic feature, which can only be
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9db3f5998ba8d95a0f57eb25c01fd324
https://doi.org/10.1109/taslp.2020.3009477
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3
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
Autor: Takaaki Saeki, Heiga Zen, Zhehuai Chen, Nobuyuki Morioka, Gary Wang, Yu Zhang, Ankur Bapna, Andrew Rosenberg, Bhuvana Ramabhadran
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models. Existing multilingual TTS typically supports tens of languages, which are a small fraction of the t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52500f6059f16130fbb660682cd9fdfb
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6
An Asynchronous WFST-Based Decoder For Automatic Speech Recognition
Autor: Hang Lv, Zhehuai Chen, Lei Xie, Daniel Povey, Sanjeev Khudanpur, Hainan Xu
Publikováno v: ICASSP
We introduce asynchronous dynamic decoder, which adopts an efficient A* algorithm to incorporate big language models in the one-pass decoding for large vocabulary continuous speech recognition. Unlike standard one-pass decoding with on-the-fly compos
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dce12b7a29dfb3225c64dd37d5b4bfc3
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9
Improving Speech Recognition Using Consistent Predictions on Synthesized Speech
Autor: Bhuvana Ramabhadran, Yu Zhang, Pedro J. Moreno, Yonghui Wu, Zhehuai Chen, Andrew Rosenberg, Gary Wang
Publikováno v: ICASSP
Speech synthesis has advanced to the point of being close to indistinguishable from human speech. However, efforts to train speech recognition systems on synthesized utterances have not been able to show that synthesized data can be effectively used
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9b8a06734331e52ecca5929918d05ebb
https://doi.org/10.1109/icassp40776.2020.9053831
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10
Sequence discriminative training for deep learning based acoustic keyword spotting
Autor: Kai Yu, Zhehuai Chen, Yanmin Qian
Publikováno v: Speech Communication. 102:100-111
Speech recognition is a sequence prediction problem. Besides employing various deep learning approaches for framelevel classification, sequence-level discriminative training has been proved to be indispensable to achieve the state-of-the-art performa
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a6b224fcb4c6b7e91425188152bef95
https://doi.org/10.1016/j.specom.2018.08.001
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