Finding continuers in Swedish Sign Language

In this paper, I use methods from corpus linguistics and computer vision to find candidates for continuers – that is, conversational markers that signal comprehension and encouragement to the primary speaker/signer to continue – in a corpus of Swedish Sign Language (STS). Using different methods based on distributional patterns in conversational turns, I identify a small set of manual signs – particularly the sign JA@ub ‘yes’ – that exhibit the characteristics associated with continuers, such as occurring frequently in repeated sequences of overlapping but noncompetitive turns. The identified signs correspond to those found in previous research on manual backchannels in STS, demonstrating that quantitative, distribution-based approaches are successful in identifying continuers. In a second step, I employ methods from computer vision to analyze a subset of the corpus videos, and find that the continuer candidates show interesting form characteristics: they are small in visible articulation and thus conversationally unobtrusive by often being articulated low and with little movement in signing space. The results show that distribution-based approaches can be used successfully with sign language corpus data, and that the nature of continuers exhibits similarities across modalities of human language.