# Homophone-based Label Smoothing in End-to-End Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2004.03437 · HTML: https://mlsanity.com/paper/2004.03437/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

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

- [Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model](https://mlsanity.com/paper/2303.06946/) (2023)
- [Perception and Semantic Aware Regularization for Sequential Confidence Calibration](https://mlsanity.com/paper/2305.19498/) (2023)

## Abstract (arXiv)

A new label smoothing method that makes use of prior knowledge of a language at human level, homophone, is proposed in this paper for automatic speech recognition (ASR). Compared with its forerunners, the proposed method uses pronunciation knowledge of homophones in a more complex way. End-to-end ASR models that learn acoustic model and language model jointly and modelling units of characters are necessary conditions for this method. Experiments with hybrid CTC sequence-to-sequence model show that the new method can reduce character error rate (CER) by 0.4% absolutely.