Zobrazeno 1 - 10
of 223
pro vyhledávání: '"WU Zelin"'
Publikováno v:
口腔疾病防治, Vol 31, Iss 9, Pp 647-652 (2023)
Objective To discuss the effectiveness and mechanism for movement of maxillary buccally transposed canines by using a door-shaped individualized dental archwire mechanic and to provide a reference for clinicians. Methods Eight patients with unilatera
Externí odkaz:
https://doaj.org/article/c6b4bb26cd9f4950b7e02f550a171b1f
Autor:
Meng, Zhong, Wu, Zelin, Prabhavalkar, Rohit, Peyser, Cal, Wang, Weiran, Chen, Nanxin, Sainath, Tara N., Ramabhadran, Bhuvana
Publikováno v:
Interspeech 2024, Kos Island, Greece
Neural contextual biasing effectively improves automatic speech recognition (ASR) for crucial phrases within a speaker's context, particularly those that are infrequent in the training data. This work proposes contextual text injection (CTI) to enhan
Externí odkaz:
http://arxiv.org/abs/2406.02921
Autor:
Wu, Zelin, Song, Gan, Li, Christopher, Rondon, Pat, Meng, Zhong, Velez, Xavier, Wang, Weiran, Caseiro, Diamantino, Pundak, Golan, Munkhdalai, Tsendsuren, Chandorkar, Angad, Prabhavalkar, Rohit
Publikováno v:
2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics - Industry Track
Contextual biasing enables speech recognizers to transcribe important phrases in the speaker's context, such as contact names, even if they are rare in, or absent from, the training data. Attention-based biasing is a leading approach which allows for
Externí odkaz:
http://arxiv.org/abs/2404.10180
Autor:
Li, Christopher, Wang, Gary, Kastner, Kyle, Su, Heng, Chen, Allen, Rosenberg, Andrew, Chen, Zhehuai, Wu, Zelin, Velikovich, Leonid, Rondon, Pat, Caseiro, Diamantino, Aleksic, Petar
Automatic speech recognition (ASR) systems can suffer from poor recall for various reasons, such as noisy audio, lack of sufficient training data, etc. Previous work has shown that recall can be improved by retrieving rewrite candidates from a large
Externí odkaz:
http://arxiv.org/abs/2401.04235
Autor:
Wang, Mingqiu, Han, Wei, Shafran, Izhak, Wu, Zelin, Chiu, Chung-Cheng, Cao, Yuan, Wang, Yongqiang, Chen, Nanxin, Zhang, Yu, Soltau, Hagen, Rubenstein, Paul, Zilka, Lukas, Yu, Dian, Meng, Zhong, Pundak, Golan, Siddhartha, Nikhil, Schalkwyk, Johan, Wu, Yonghui
We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-modal model that takes advantage of pretrained foundational speech and language models. SLM freezes the pretrained foundation models to maximally preserves their
Externí odkaz:
http://arxiv.org/abs/2310.00230
Autor:
Wang, Weiran, Wu, Zelin, Caseiro, Diamantino, Munkhdalai, Tsendsuren, Sim, Khe Chai, Rondon, Pat, Pundak, Golan, Song, Gan, Prabhavalkar, Rohit, Meng, Zhong, Zhao, Ding, Sainath, Tara, Mengibar, Pedro Moreno
Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios. We propose algorithms for contextual biasing based on the Knuth-
Externí odkaz:
http://arxiv.org/abs/2310.00178
We propose a new two-pass E2E speech recognition model that improves ASR performance by training on a combination of paired data and unpaired text data. Previously, the joint acoustic and text decoder (JATD) has shown promising results through the us
Externí odkaz:
http://arxiv.org/abs/2303.15293
Autor:
Chang, Shuo-yiin, Prakash, Guru, Wu, Zelin, Liang, Qiao, Sainath, Tara N., Li, Bo, Stambler, Adam, Upadhyay, Shyam, Faruqui, Manaal, Strohman, Trevor
In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query.However, speaking queries followed by a hotword each timeintroduces a cognitive burden in continued conversations. Toavoid repe
Externí odkaz:
http://arxiv.org/abs/2208.13322
Autor:
Yu, Chunxiu, Wu, Zelin, Shi, Hongle, Gu, Lingyun, Chen, Kexin, He, Chuan-Shu, Liu, Yang, Zhang, Heng, Zhou, Peng, Xiong, Zhaokun, Lai, Bo
Publikováno v:
In Chinese Chemical Letters August 2024 35(8)
Autor:
Wu, Zelin, Dong, Pengxin, Huang, Yifan, Chen, Yao, Liu, Runze, Cao, Quanliang, Li, Liang, Wang, Hao, Han, Xiaotao, Wang, Qiuliang
Publikováno v:
In Journal of Materials Research and Technology July-August 2024 31:2435-2449