  Automatic speech recognition and spoken dialogue systems have made great
advances through the use of deep machine learning methods. This is partly due
to greater computing power but also through the large amount of data available
in common languages, such as English. Conversely, research in minority
languages, including sign languages, is hampered by the severe lack of data.
This has led to work on transfer learning methods, whereby a model developed
for one language is reused as the starting point for a model on a second
language, which is less resourced. In this paper, we examine two transfer
learning techniques of fine-tuning and layer substitution for language
modelling of British Sign Language. Our results show improvement in perplexity
when using transfer learning with standard stacked LSTM models, trained
initially using a large corpus for standard English from the Penn Treebank
corpus
