Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Rondon, Pat"'
Autor:
Velikovich, Leonid, Li, Christopher, Caseiro, Diamantino, Kumar, Shankar, Rondon, Pat, Joshi, Kandarp, Velez, Xavier
For end-to-end Automatic Speech Recognition (ASR) models, recognizing personal or rare phrases can be hard. A promising way to improve accuracy is through spelling correction (or rewriting) of the ASR lattice, where potentially misrecognized phrases
Externí odkaz:
http://arxiv.org/abs/2409.16469
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, 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
Autor:
Shen, Jonathan, Nguyen, Patrick, Wu, Yonghui, Chen, Zhifeng, Chen, Mia X., Jia, Ye, Kannan, Anjuli, Sainath, Tara, Cao, Yuan, Chiu, Chung-Cheng, He, Yanzhang, Chorowski, Jan, Hinsu, Smit, Laurenzo, Stella, Qin, James, Firat, Orhan, Macherey, Wolfgang, Gupta, Suyog, Bapna, Ankur, Zhang, Shuyuan, Pang, Ruoming, Weiss, Ron J., Prabhavalkar, Rohit, Liang, Qiao, Jacob, Benoit, Liang, Bowen, Lee, HyoukJoong, Chelba, Ciprian, Jean, Sébastien, Li, Bo, Johnson, Melvin, Anil, Rohan, Tibrewal, Rajat, Liu, Xiaobing, Eriguchi, Akiko, Jaitly, Navdeep, Ari, Naveen, Cherry, Colin, Haghani, Parisa, Good, Otavio, Cheng, Youlong, Alvarez, Raziel, Caswell, Isaac, Hsu, Wei-Ning, Yang, Zongheng, Wang, Kuan-Chieh, Gonina, Ekaterina, Tomanek, Katrin, Vanik, Ben, Wu, Zelin, Jones, Llion, Schuster, Mike, Huang, Yanping, Chen, Dehao, Irie, Kazuki, Foster, George, Richardson, John, Macherey, Klaus, Bruguier, Antoine, Zen, Heiga, Raffel, Colin, Kumar, Shankar, Rao, Kanishka, Rybach, David, Murray, Matthew, Peddinti, Vijayaditya, Krikun, Maxim, Bacchiani, Michiel A. U., Jablin, Thomas B., Suderman, Rob, Williams, Ian, Lee, Benjamin, Bhatia, Deepti, Carlson, Justin, Yavuz, Semih, Zhang, Yu, McGraw, Ian, Galkin, Max, Ge, Qi, Pundak, Golan, Whipkey, Chad, Wang, Todd, Alon, Uri, Lepikhin, Dmitry, Tian, Ye, Sabour, Sara, Chan, William, Toshniwal, Shubham, Liao, Baohua, Nirschl, Michael, Rondon, Pat
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily ex
Externí odkaz:
http://arxiv.org/abs/1902.08295