Aligning Subtitles in Sign Language Videos

TL;DR

This paper introduces a Transformer-based model that accurately aligns subtitles with sign language videos at the frame level, enabling improved synchronization for sign language translation.

Contribution

We develop a novel Transformer architecture utilizing BERT and CNN embeddings to localize entire subtitles in continuous sign language videos, surpassing previous keyword-based methods.

Findings

Significant improvement over baseline alignment methods

Effective use of BERT and CNN embeddings for encoding signals

Potential to enhance machine translation of sign languages

Abstract

The goal of this work is to temporally align asynchronous subtitles in sign language videos. In particular, we focus on sign-language interpreted TV broadcast data comprising (i) a video of continuous signing, and (ii) subtitles corresponding to the audio content. Previous work exploiting such weakly-aligned data only considered finding keyword-sign correspondences, whereas we aim to localise a complete subtitle text in continuous signing. We propose a Transformer architecture tailored for this task, which we train on manually annotated alignments covering over 15K subtitles that span 17.7 hours of video. We use BERT subtitle embeddings and CNN video representations learned for sign recognition to encode the two signals, which interact through a series of attention layers. Our model outputs frame-level predictions, i.e., for each video frame, whether it belongs to the queried subtitle or not. Through extensive evaluations, we show substantial improvements over existing alignment baselines that do not make use of subtitle text embeddings for learning. Our automatic alignment model opens up possibilities for advancing machine translation of sign languages via providing continuously synchronized video-text data.