Estudio de bases de datos para el reconocimiento automático de lenguas de signos

Autores/as

  • Darío Tilves Santiago
  • Carmén García Mateo
  • Soledad Torres Guijarro
  • Laura Docío Fernández
  • José Luis Alba Castro

DOI:

https://doi.org/10.35869/hafh.v22i0.1658

Palabras clave:

Deaf, Sign Language Dataset, Automatic Recognition, ASLR, RALS, Annotation, ELAN, ANVIL

Resumen

Automatic sign language recognition (ASLR) is quite a complex task, not only for the difficulty of dealing with very dynamic video information, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (LSE) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, some problems of machine-learning- based ASLR are addressed. A review of publicly available datasets is given and a new one is presented. It is also discussed the current annotations methods and annotation programs. In our review of existing datasets, our main conclusion is that there is a need for more with high-quality data and annotations.

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Publicado

2020-03-13

Cómo citar

Tilves Santiago, D., García Mateo, C., Torres Guijarro, S., Docío Fernández, L., & Alba Castro, J. L. (2020). Estudio de bases de datos para el reconocimiento automático de lenguas de signos. Hesperia: Anuario De Filología Hispánica, 22, 145–160. https://doi.org/10.35869/hafh.v22i0.1658

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