# Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?

arXiv: https://arxiv.org/abs/2004.05985 · HTML: https://mlsanity.com/paper/2004.05985/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

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

- [Capitalization and Punctuation Restoration: a Survey](https://mlsanity.com/paper/2111.10746/) (2021)
- [Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios](https://mlsanity.com/paper/2109.06103/) (2021)
- [A light-weight and efficient punctuation and word casing prediction model for on-device streaming ASR](https://mlsanity.com/paper/2407.13142/) (2024)
- [Enhancing Fully Formatted End-to-End Speech Recognition with Knowledge Distillation via Multi-Codebook Vector Quantization](https://mlsanity.com/paper/2512.18967/) (2025)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) systems introduce word errors, which often confuse punctuation prediction models, turning punctuation restoration into a challenging task. These errors usually take the form of homonyms. We show how retrofitting of the word embeddings on the domain-specific data can mitigate ASR errors. Our main contribution is a method for better alignment of homonym embeddings and the validation of the presented method on the punctuation prediction task. We record the absolute improvement in punctuation prediction accuracy between 6.2% (for question marks) to 9% (for periods) when compared with the state-of-the-art model.