Towards a Video Corpus for Signer-Independent Continuous Sign Language Recognition

Research in the field of continuous sign language recognitio n has not yet addressed the problem of interpersonal variance in sign ing. Applied to signerindependent tasks, current recognition systems show poor p erformance as their training bases upon corpora with an insufficient number of si gners. In contrast to speech recognition, there is actually no benchmark which me ets the requirements for signer-independent recognition. Because of this absen ce we currently record a video corpus based on a vocabulary of 450 basic signs in Germa n Sign Language. The corpus comprises 780 sentences each articulated by 20 di fferent signers. The whole database will be made available for interested resear ch rs.

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