A SIGN TO SPEECH TRANSLATION SYSTEM USING MATCHER NEURAL NETWORKS
Sign language translation is an interesting but difficult problem for which neural network techniques seem promising because of their ability to adjust to the user's hand movements, which is not possible to do by most other techniques. However, even using neural networks and artificial sign languages, the translation is hard, and the best known system, that of Fels Hinton (1993) is capable of translating only 66 root signs and their conjugations. This paper improves their results to 395 root signs while preserving a high accuracy (i.e. over 96 in translation. The use of matcher neural networks (Revesz 1989, 1990) and asymmetric Hamming distances are the key sources of improvement.