  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.
