Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Kao, Justine"'
Autor:
JU, Da, Jiang, Song, Cohen, Andrew, Foss, Aaron, Mitts, Sasha, Zharmagambetov, Arman, Amos, Brandon, Li, Xian, Kao, Justine T, Fazel-Zarandi, Maryam, Tian, Yuandong
Publikováno v:
EMNLP 2024 Demo Track
Travel planning is a challenging and time-consuming task that aims to find an itinerary which satisfies multiple, interdependent constraints regarding flights, accommodations, attractions, and other travel arrangements. In this paper, we propose To t
Externí odkaz:
http://arxiv.org/abs/2410.16456
Autor:
Communication, Seamless, Barrault, Loïc, Chung, Yu-An, Meglioli, Mariano Coria, Dale, David, Dong, Ning, Duppenthaler, Mark, Duquenne, Paul-Ambroise, Ellis, Brian, Elsahar, Hady, Haaheim, Justin, Hoffman, John, Hwang, Min-Jae, Inaguma, Hirofumi, Klaiber, Christopher, Kulikov, Ilia, Li, Pengwei, Licht, Daniel, Maillard, Jean, Mavlyutov, Ruslan, Rakotoarison, Alice, Sadagopan, Kaushik Ram, Ramakrishnan, Abinesh, Tran, Tuan, Wenzek, Guillaume, Yang, Yilin, Ye, Ethan, Evtimov, Ivan, Fernandez, Pierre, Gao, Cynthia, Hansanti, Prangthip, Kalbassi, Elahe, Kallet, Amanda, Kozhevnikov, Artyom, Gonzalez, Gabriel Mejia, Roman, Robin San, Touret, Christophe, Wong, Corinne, Wood, Carleigh, Yu, Bokai, Andrews, Pierre, Balioglu, Can, Chen, Peng-Jen, Costa-jussà, Marta R., Elbayad, Maha, Gong, Hongyu, Guzmán, Francisco, Heffernan, Kevin, Jain, Somya, Kao, Justine, Lee, Ann, Ma, Xutai, Mourachko, Alex, Peloquin, Benjamin, Pino, Juan, Popuri, Sravya, Ropers, Christophe, Saleem, Safiyyah, Schwenk, Holger, Sun, Anna, Tomasello, Paden, Wang, Changhan, Wang, Jeff, Wang, Skyler, Williamson, Mary
Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this work, we introduce a family of models that enable end-to-end expressive
Externí odkaz:
http://arxiv.org/abs/2312.05187
Autor:
Communication, Seamless, Barrault, Loïc, Chung, Yu-An, Meglioli, Mariano Cora, Dale, David, Dong, Ning, Duquenne, Paul-Ambroise, Elsahar, Hady, Gong, Hongyu, Heffernan, Kevin, Hoffman, John, Klaiber, Christopher, Li, Pengwei, Licht, Daniel, Maillard, Jean, Rakotoarison, Alice, Sadagopan, Kaushik Ram, Wenzek, Guillaume, Ye, Ethan, Akula, Bapi, Chen, Peng-Jen, Hachem, Naji El, Ellis, Brian, Gonzalez, Gabriel Mejia, Haaheim, Justin, Hansanti, Prangthip, Howes, Russ, Huang, Bernie, Hwang, Min-Jae, Inaguma, Hirofumi, Jain, Somya, Kalbassi, Elahe, Kallet, Amanda, Kulikov, Ilia, Lam, Janice, Li, Daniel, Ma, Xutai, Mavlyutov, Ruslan, Peloquin, Benjamin, Ramadan, Mohamed, Ramakrishnan, Abinesh, Sun, Anna, Tran, Kevin, Tran, Tuan, Tufanov, Igor, Vogeti, Vish, Wood, Carleigh, Yang, Yilin, Yu, Bokai, Andrews, Pierre, Balioglu, Can, Costa-jussà, Marta R., Celebi, Onur, Elbayad, Maha, Gao, Cynthia, Guzmán, Francisco, Kao, Justine, Lee, Ann, Mourachko, Alexandre, Pino, Juan, Popuri, Sravya, Ropers, Christophe, Saleem, Safiyyah, Schwenk, Holger, Tomasello, Paden, Wang, Changhan, Wang, Jeff, Wang, Skyler
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-
Externí odkaz:
http://arxiv.org/abs/2308.11596
Autor:
Huang, Wen-Chin, Peloquin, Benjamin, Kao, Justine, Wang, Changhan, Gong, Hongyu, Salesky, Elizabeth, Adi, Yossi, Lee, Ann, Chen, Peng-Jen
Expressive speech-to-speech translation (S2ST) aims to transfer prosodic attributes of source speech to target speech while maintaining translation accuracy. Existing research in expressive S2ST is limited, typically focusing on a single expressivity
Externí odkaz:
http://arxiv.org/abs/2301.10606
Autor:
Chen, Mingda, Duquenne, Paul-Ambroise, Andrews, Pierre, Kao, Justine, Mourachko, Alexandre, Schwenk, Holger, Costa-jussà, Marta R.
End-to-End speech-to-speech translation (S2ST) is generally evaluated with text-based metrics. This means that generated speech has to be automatically transcribed, making the evaluation dependent on the availability and quality of automatic speech r
Externí odkaz:
http://arxiv.org/abs/2212.08486
Autor:
Chen, Peng-Jen, Tran, Kevin, Yang, Yilin, Du, Jingfei, Kao, Justine, Chung, Yu-An, Tomasello, Paden, Duquenne, Paul-Ambroise, Schwenk, Holger, Gong, Hongyu, Inaguma, Hirofumi, Popuri, Sravya, Wang, Changhan, Pino, Juan, Hsu, Wei-Ning, Lee, Ann
We study speech-to-speech translation (S2ST) that translates speech from one language into another language and focuses on building systems to support languages without standard text writing systems. We use English-Taiwanese Hokkien as a case study,
Externí odkaz:
http://arxiv.org/abs/2211.06474
Autor:
Muralidharan, Deepak, Moniz, Joel Ruben Antony, Gao, Sida, Yang, Xiao, Kao, Justine, Pulman, Stephen, Kothari, Atish, Shen, Ray, Pan, Yinying, Kaul, Vivek, Ibrahim, Mubarak Seyed, Xiang, Gang, Dun, Nan, Zhou, Yidan, O, Andy, Zhang, Yuan, Chitkara, Pooja, Wang, Xuan, Patel, Alkesh, Tayal, Kushal, Zheng, Roger, Grasch, Peter, Williams, Jason D., Li, Lin
Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that
Externí odkaz:
http://arxiv.org/abs/2005.14408
Autor:
Muralidharan, Deepak, Kao, Justine, Yang, Xiao, Li, Lin, Viswanathan, Lavanya, Ibrahim, Mubarak Seyed, Luikens, Kevin, Pulman, Stephen, Garg, Ashish, Kothari, Atish, Williams, Jason
Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in these systems generally rely on supervised machine learning algorithms tha
Externí odkaz:
http://arxiv.org/abs/1909.09143
Autor:
Chen, Xi C., Sagar, Adithya, Kao, Justine T., Li, Tony Y., Klein, Christopher, Pulman, Stephen, Garg, Ashish, Williams, Jason D.
We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data for any ML system typically improves accuracy, but only if it provides e
Externí odkaz:
http://arxiv.org/abs/1908.11404
Autor:
Kao, Justine Shu-Ting
Publikováno v:
Anafora - časopis za znanost o književnosti / Anafora - Academic Literary Journal. 6(2):329-346
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=818949
