Modular connectionist structure for 100-word recognition
Heidi Hackbarth, Juval Mantel · 2002
A modular neural structure is presented which is dedicated to the recognition of larger vocabularies. It contains several so-called scaly subnets, assembled into a compound network by neural glue elements. This architecture and the corresponding training scheme have been investigated for various network parameters during speaker-dependent 100-word recognition. Important simulation results were compared with a multilayer perceptron showing scaly input-to-hidden connections and with standard dynamic time warping. Under the criterion of high recognition rates along with very short reaction time, modular subnet assemblies provide for successful recognition of 100 words. They are also recommended for speaker-independent classification.>