no code implementations • 23 Mar 2023 • Sepand Mavandadi, Tara N. Sainath, Ke Hu, Zelin Wu
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.
no code implementations • 13 Sep 2022 • Chao Zhang, Bo Li, Tara Sainath, Trevor Strohman, Sepand Mavandadi, Shuo-Yiin Chang, Parisa Haghani
Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additional task.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 29 Jun 2022 • Ke Hu, Tara N. Sainath, Yanzhang He, Rohit Prabhavalkar, Trevor Strohman, Sepand Mavandadi, Weiran Wang
Text-only and semi-supervised training based on audio-only data has gained popularity recently due to the wide availability of unlabeled text and speech data.
no code implementations • 15 Apr 2022 • Weiran Wang, Tongzhou Chen, Tara N. Sainath, Ehsan Variani, Rohit Prabhavalkar, Ronny Huang, Bhuvana Ramabhadran, Neeraj Gaur, Sepand Mavandadi, Cal Peyser, Trevor Strohman, Yanzhang He, David Rybach
Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the rescoring setups.
no code implementations • 24 Aug 2020 • Cal Peyser, Sepand Mavandadi, Tara N. Sainath, James Apfel, Ruoming Pang, Shankar Kumar
End-to-end (E2E) automatic speech recognition (ASR) systems lack the distinct language model (LM) component that characterizes traditional speech systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2