This paper reports an implementation of dynamic programming based time-normalized algorithm

called dynamic time warping （DTW）

on neural networks. DTW is one of the most successful algorithms for spoken word recognition. It is very robust and usually provides the highest recognition rate possible but it takes a lot of computer time unless it is implemented by special hardware. In this implementation

the computation is governed by two recurrent subnets and one memory layer

demonstrating a hard-wiring mechanism which benefits from existing approaches.