Pseudo-segment based speech recognition using neural recurrent whole-word recognizers

Describes a recurrent neural network based, isolated word speech recognizer. The recognizer uses 2 MLPs. A first, static MLP is used for classification of frames in phonemes. Next, a time compression step is applied. The resulting pseudo-segments are then used as inputs for a second, dynamic MLP that integrates the information over time to decide the current word. The authors apply this approach on an isolated digit recognition task and compare the results with hybrid MLP/HMM approach using the same static MLP