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

arXiv: https://arxiv.org/abs/1912.04784 · HTML: https://mlsanity.com/paper/1912.04784/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [CTC Variations Through New WFST Topologies](https://mlsanity.com/paper/2110.03098/) (2021)

## Abstract (arXiv)

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.