A novel approach to American Sign Language (ASL) phrase verification using reversed signing

We propose a novel approach for American Sign Langauge (ASL) phrase verification that combines confidence measures (CM) obtained from aligning forward sign models (the conventional approach) to the input data with the CM´s obtained from aligning reversed sign models to the same input. To demonstrate our approach we have used two CM´s, the Normalized likelihood score and the Log-Likelihood Ratio (LLR).We perform leave-one-signer-out cross validation on a dataset of 420 ASL phrases obtained from five deaf children playing an educational game called CopyCat. The results show that for the new method the alignment selected for signs in a test phrase has a significantly better match to the ground truth when compared to the traditional approach. Additionally, when a low false reject rate is desired the new technique can provide a better verification accuracy as compared to the conventional approach.