arXiv:2512.08094v2 Announce Type: replace Abstract: The goal of this work is to develop a universal approach for aligning subtitles (i.e., spoken language text with corresponding timestamps) to continuous sign language videos. Prior approaches typically rely on end-to-end training tied to a specific language or dataset, which limits their generality. In contrast, our method Segment, Embed, and Align (SEA) provides a single framework that works across multiple languages and domains. SEA leverages two pretrained models: the first to segment a video frame sequence into individual signs and the se
The proliferation of advanced AI models and increased accessibility of computational resources are enabling more generalized and robust solutions for complex linguistic alignment tasks.
This breakthrough addresses a significant challenge in bridging communication gaps for the deaf community and opens new avenues for inclusive AI applications that were previously limited by language-specific models.
The ability to universally align subtitles to sign language videos across multiple languages without retraining marks a shift from narrow-AI solutions to more generalizable and adaptable frameworks.
- · Deaf and hard-of-hearing communities
- · AI researchers in NLP and computer vision
- · Developers of inclusive communication technologies
- · Content creators and media platforms
- · Developers of highly specialized, single-language sign translation models
Improved accessibility and integration of sign language into digital content and communication.
Accelerated development of real-time sign language translation and interpretation systems, benefiting education and public services.
The establishment of universal standards for sign language AI, driven by broad adoption and more robust general models, further reducing communication barriers globally.
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