Searching for fingerspelled content in American Sign Language
This addresses the need for making AI technologies accessible to deaf individuals by enabling search in sign language videos, focusing on a previously unstudied task.
The paper tackles the problem of searching for fingerspelled keywords or phrases in raw American Sign Language videos, proposing FSS-Net, an end-to-end model that jointly detects fingerspelling and matches it to text sequences, and shows it significantly outperforms baseline methods on a large public dataset.
Natural language processing for sign language video - including tasks like recognition, translation, and search - is crucial for making artificial intelligence technologies accessible to deaf individuals, and is gaining research interest in recent years. In this paper, we address the problem of searching for fingerspelled key-words or key phrases in raw sign language videos. This is an important task since significant content in sign language is often conveyed via fingerspelling, and to our knowledge the task has not been studied before. We propose an end-to-end model for this task, FSS-Net, that jointly detects fingerspelling and matches it to a text sequence. Our experiments, done on a large public dataset of ASL fingerspelling in the wild, show the importance of fingerspelling detection as a component of a search and retrieval model. Our model significantly outperforms baseline methods adapted from prior work on related tasks