KL: A neural model for capturing structure in speech
In this paper we propose a neural model conceived for problems of word recognition and understanding of small protocol-driven sentences. The model is based on an unified approach to integrate priori knowledge and learning by example. The priori knowledge, injected into the network connections, can be of different levels, while learning is mainly conceived as a refinement process, and is responsible of dealing with uncertainty. We describe a small prototype for problems of isolated word recognition.
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