# Subword and Crossword Units for CTC Acoustic Models

arXiv: https://arxiv.org/abs/1712.06855 · HTML: https://mlsanity.com/paper/1712.06855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

## Built on by (most cited first, 2 of 2)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [Pronunciation-aware unique character encoding for RNN Transducer-based Mandarin speech recognition](https://mlsanity.com/paper/2207.14578/) (2022)

## Abstract (arXiv)

This paper proposes a novel approach to create an unit set for CTC based speech recognition systems. By using Byte Pair Encoding we learn an unit set of an arbitrary size on a given training text. In contrast to using characters or words as units this allows us to find a good trade-off between the size of our unit set and the available training data. We evaluate both Crossword units, that may span multiple word, and Subword units. By combining this approach with decoding methods using a separate language model we are able to achieve state of the art results for grapheme based CTC systems.