Machine Translation from Signed to Spoken Languages: State of the Art and Challenges

TL;DR

This paper reviews the current state of automatic sign to spoken language translation, highlighting recent advances, challenges, and the need for interdisciplinary, linguistically motivated approaches involving end users.

Contribution

It provides a comprehensive overview of the field, identifies key challenges, and advocates for integrating linguistic analysis and user involvement in future research.

Findings

Sign language translation research has advanced recently but lacks linguistic motivation.

Current methods often do not account for sign language's unique modality.

Interdisciplinary collaboration and user-centered design are crucial for progress.

Abstract

Automatic translation from signed to spoken languages is an interdisciplinary research domain, lying on the intersection of computer vision, machine translation and linguistics. Nevertheless, research in this domain is performed mostly by computer scientists in isolation. As the domain is becoming increasingly popular - the majority of scientific papers on the topic of sign language translation have been published in the past three years - we provide an overview of the state of the art as well as some required background in the different related disciplines. We give a high-level introduction to sign language linguistics and machine translation to illustrate the requirements of automatic sign language translation. We present a systematic literature review to illustrate the state of the art in the domain and then, harking back to the requirements, lay out several challenges for future research. We find that significant advances have been made on the shoulders of spoken language machine translation research. However, current approaches are often not linguistically motivated or are not adapted to the different input modality of sign languages. We explore challenges related to the representation of sign language data, the collection of datasets, the need for interdisciplinary research and requirements for moving beyond research, towards applications. Based on our findings, we advocate for interdisciplinary research and to base future research on linguistic analysis of sign languages. Furthermore, the inclusion of deaf and hearing end users of sign language translation applications in use case identification, data collection and evaluation is of the utmost importance in the creation of useful sign language translation models. We recommend iterative, human-in-the-loop, design and development of sign language translation models.