# Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units

arXiv: https://arxiv.org/abs/1807.04978 · HTML: https://mlsanity.com/paper/1807.04978/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision](https://mlsanity.com/paper/2406.02166/) (2024)
- [Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching](https://mlsanity.com/paper/2112.10202/) (2021)
- [Memory-augmented conformer for improved end-to-end long-form ASR](https://mlsanity.com/paper/2309.13029/) (2023)

## Abstract (arXiv)

In this paper, we present an end-to-end automatic speech recognition system, which successfully employs subword units in a hybrid CTC-Attention based system. The subword units are obtained by the byte-pair encoding (BPE) compression algorithm. Compared to using words as modeling units, using characters or subword units does not suffer from the out-of-vocabulary (OOV) problem. Furthermore, using subword units further offers a capability in modeling longer context than using characters. We evaluate different systems over the LibriSpeech 1000h dataset. The subword-based hybrid CTC-Attention system obtains 6.8% word error rate (WER) on the test_clean subset without any dictionary or external language model. This represents a significant improvement (a 12.8% WER relative reduction) over the character-based hybrid CTC-Attention system.