1. [Publications](/publications)
2. Cross-Language Transfer Learning and Domain Adaptation for End-to-End Automatic Speech Recognition
 
 # Cross-Language Transfer Learning and Domain Adaptation for End-to-End Automatic Speech Recognition

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 In this paper, we demonstrate the efficacy of transfer learning and continuous learning for various automatic speech recognition (ASR) tasks using end-to-end models trained with CTC loss. We start with a large pre-trained English ASR model and show that transfer learning can be effectively and easily performed on: (1) different English accents, (2) different languages (from English to German, Spanish, Russian, or from Mandarin to Cantonese) and (3) application-specific domains. Our extensive set of experiments demonstrate that in all three cases, transfer learning from a good base model has higher accuracy than a model trained from scratch. Our results indicate that, for fine-tuning, larger pre-trained models are better than small pre-trained models, even if the dataset for fine-tuning is small. We also show that transfer learning significantly speeds up convergence, which could result in significant cost savings when training with large datasets.



 ## Authors



Jocelyn Huang (NVIDIA)

Oleksii Kuchaiev (NVIDIA)

Patrick O’Neill (Kensho)

Vitaly Lavrukhin (NVIDIA)

Jason Li (NVIDIA)

Adriana Flores (NVIDIA)

Georg Kucsko (Kensho)

Boris Ginsburg (NVIDIA)

 

 

 ## Publication Date



Friday, May 8, 2020

 

 ## Research Area



[Speech Processing](/research-area/speech-processing)

 

 

 ## External Links



[Paper](https://arxiv.org/abs/2005.04290)