A survey indexes 120 sign-language datasets from 35 languages, identifies modality, annotation, and bias issues, and proposes a standardized 24-field datasheet with an open repository.
European Conference on Computer Vision , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
NEST-V1 demonstrates feasibility of emotion-aware Nepali sign language avatar generation from speech with 81.1% ASR accuracy and 79.21% emotion accuracy on a small dataset using an efficient 22.1M parameter model.
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Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards
A survey indexes 120 sign-language datasets from 35 languages, identifies modality, annotation, and bias issues, and proposes a standardized 24-field datasheet with an open repository.
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Low Resource Multimodal Translation of Nepali Spoken Words into Emotion-Conditioned Sign Language Avatars
NEST-V1 demonstrates feasibility of emotion-aware Nepali sign language avatar generation from speech with 81.1% ASR accuracy and 79.21% emotion accuracy on a small dataset using an efficient 22.1M parameter model.