Hypothesis comparison guided cross validation for unsupervised signer adaptation

Abstract

Signer adaptation is important to sign language recognition systems in that a one-size-fits-all model set can not perform well on all kinds of signers. Supervised signer adaptation must utilize the labeled adaptation data that are collected explicitly. To skip the data collecting process in signer adaptation, we propose an unsupervised adaptation method called hypothesis comparison guided cross validation (HC-CV) algorithm. The algorithm not only addresses the problem of overlap between the data set to be labeled and the data set for adaptation, but also employs an additional hypothesis comparison step to decrease the noise rate of the adaptation data set. Experimental results show that the HC-CV adaptation algorithm is superior to the CV adaptation algorithm and the conventional self-teaching algorithm. Though the algorithm is proposed for signer adaptation, it can also be applied to speaker adaptation and writer adaptation straightforwardly.