A Novel Topology for End-to-end Temporal Classification and Segmentation with Recurrent Neural Network

Understand

Connectionist temporal classification (CTC) has matured as an alignment free to sequence transduction and shows competitive for end-to-end speech recognition.

• In the CTC topology, the blank symbol occupies more than half of the state trellis, which results the spike phenomenon of the non-blank symbols.
• For classification task, the spikes work quite well, but as to the segmentation task it does not provide boundaries information.
• In this paper, a novel topology is introduced to combine the temporal classification and segmentation ability in one framework.