Unsupervised Sign Language Phoneme Clustering using HamNoSys Notation

Autor:	Mocialov, Boris, Turner, Graham, Hastie, Helen
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Traditionally, sign language resources have been collected in controlled settings for specific tasks involving supervised sign classification or linguistic studies accompanied by specific annotation type. To date, very few who explored signing videos found online on social media platforms as well as the use of unsupervised methods applied to such resources. Due to the fact that the field is striving to achieve acceptable model performance on the data that differs from that seen during training calls for more diversity in sign language data, stepping away from the data obtained in controlled laboratory settings. Moreover, since the sign language data collection and annotation carries large overheads, it is desirable to accelerate the annotation process. Considering the aforementioned tendencies, this paper takes the side of harvesting online data in a pursuit for automatically generating and annotating sign language corpora through phoneme clustering. Comment: 11 pages, 10 figures, VarDial2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.10560 Zobrazit plný text záznamu View this record from Arxiv