IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task

06/30/2021
by   Pavel Denisov, et al.
University of Stuttgart
0

This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team. We utilize state-of-the-art models combined with several data augmentation, multi-task and transfer learning approaches for the automatic speech recognition (ASR) and machine translation (MT) steps of our cascaded system. Moreover, we also explore the feasibility of a full end-to-end speech translation (ST) model in the case of very constrained amount of ground truth labeled data. Our best system achieves the best performance among all submitted systems for Congolese Swahili to English and French with BLEU scores 7.7 and 13.7 respectively, and the second best result for Coastal Swahili to English with BLEU score 14.9.

READ FULL TEXT

page 1

page 2

page 3

page 4

09/14/2019

Leveraging Out-of-Task Data for End-to-End Automatic Speech Translation

For automatic speech translation (AST), end-to-end approaches are outper...
05/04/2022

ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks

This paper describes the ON-TRAC Consortium translation systems develope...
05/27/2020

Phone Features Improve Speech Translation

End-to-end models for speech translation (ST) more tightly couple speech...
10/18/2022

Simple and Effective Unsupervised Speech Translation

The amount of labeled data to train models for speech tasks is limited f...
10/26/2022

Improving Speech-to-Speech Translation Through Unlabeled Text

Direct speech-to-speech translation (S2ST) is among the most challenging...
07/24/2021

The USYD-JD Speech Translation System for IWSLT 2021

This paper describes the University of Sydney JD's joint submission o...
02/24/2019

The ARIEL-CMU Systems for LoReHLT18

This paper describes the ARIEL-CMU submissions to the Low Resource Human...

Please sign up or login with your details

Forgot password? Click here to reset