Pause detection in continuous sign language

Sign language segmentation breaks a continuous sentence into its basic lexical units by detecting word boundaries. For robust recognition, the majority of direct segmentation approaches exploit these inter-sign pauses in a stream of hand gestures to demarcate word boundaries. Recent attempts to segment a continuous discourse exploit the constancy or directional variations of sign parameters (mainly spatial parameters). The delayed absolute difference (DAD) signature of hand positions provides means for analysing the segmentation features like pauses, repetitions and directional variations in a unique tool. In this paper, a DAD-based pause detection algorithm has been described. The performance of this deterministic algorithm is compared with three segmentation approaches. All the experiments and comparisons are done using the subjective annotation by 15 native New Zealand Sign Language (NZSL) signers. The proposed algorithm correctly and consistently detected the various lengths of pauses as compared to the existing segmentation approaches.