Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Siddhartha, Nikhil"'
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:
Bai, Junwen, Li, Bo, Zhang, Yu, Bapna, Ankur, Siddhartha, Nikhil, Sim, Khe Chai, Sainath, Tara N.
Self-supervised training has shown promising gains in pretraining models and facilitating the downstream finetuning for speech recognition, like multilingual ASR. Most existing methods adopt a 2-stage scheme where the self-supervised loss is optimize
Externí odkaz:
http://arxiv.org/abs/2111.08137
Autor:
Hwang, Dongseong, Misra, Ananya, Huo, Zhouyuan, Siddhartha, Nikhil, Garg, Shefali, Qiu, David, Sim, Khe Chai, Strohman, Trevor, Beaufays, Françoise, He, Yanzhang
Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance. However, the approach mostly focus on in-domain performance for public datasets. In this study, we utilize th
Externí odkaz:
http://arxiv.org/abs/2110.00165
Autor:
Huo, Zhouyuan, Hwang, Dongseong, Sim, Khe Chai, Garg, Shefali, Misra, Ananya, Siddhartha, Nikhil, Strohman, Trevor, Beaufays, Françoise
Streaming end-to-end speech recognition models have been widely applied to mobile devices and show significant improvement in efficiency. These models are typically trained on the server using transcribed speech data. However, the server data distrib
Externí odkaz:
http://arxiv.org/abs/2110.00155