SLM: Bridge the thin gap between speech and text foundation models

no code implementations • 30 Sep 2023 • Mingqiu Wang, Wei Han, Izhak Shafran, Zelin Wu, Chung-Cheng Chiu, Yuan Cao, Yongqiang Wang, Nanxin Chen, Yu Zhang, Hagen Soltau, Paul Rubenstein, Lukas Zilka, Dian Yu, Zhong Meng, Golan Pundak, Nikhil Siddhartha, Johan Schalkwyk, Yonghui Wu

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.

Instruction Following Language Modelling +3

Joint Unsupervised and Supervised Training for Multilingual ASR

no code implementations • 15 Nov 2021 • Junwen Bai, Bo Li, Yu Zhang, Ankur Bapna, Nikhil Siddhartha, Khe Chai Sim, Tara N. Sainath

Our average WER of all languages outperforms average monolingual baseline by 33. 3%, and the state-of-the-art 2-stage XLSR by 32%.

Language Modelling Masked Language Modeling +3

Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device

no code implementations • 1 Oct 2021 • Zhouyuan Huo, Dongseong Hwang, Khe Chai Sim, Shefali Garg, Ananya Misra, Nikhil Siddhartha, Trevor Strohman, Françoise Beaufays

These models are typically trained on the server using transcribed speech data.

Domain Adaptation Self-Supervised Learning +2

Large-scale ASR Domain Adaptation using Self- and Semi-supervised Learning

no code implementations • 1 Oct 2021 • Dongseong Hwang, Ananya Misra, Zhouyuan Huo, Nikhil Siddhartha, Shefali Garg, David Qiu, Khe Chai Sim, Trevor Strohman, Françoise Beaufays, Yanzhang He

Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance.

Domain Adaptation