# Robust Prediction of Punctuation and Truecasing for Medical ASR

arXiv: https://arxiv.org/abs/2007.02025 · HTML: https://mlsanity.com/paper/2007.02025/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR](https://mlsanity.com/paper/2310.00274/) (2023)
- [Capitalization and Punctuation Restoration: a Survey](https://mlsanity.com/paper/2111.10746/) (2021)
- [Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction](https://mlsanity.com/paper/2104.02219/) (2021)
- [MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder](https://mlsanity.com/paper/2409.14074/) (2024)
- [Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios](https://mlsanity.com/paper/2109.06103/) (2021)
- [Four-in-One: A Joint Approach to Inverse Text Normalization, Punctuation, Capitalization, and Disfluency for Automatic Speech Recognition](https://mlsanity.com/paper/2210.15063/) (2022)
- [Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model](https://mlsanity.com/paper/2202.08171/) (2022)
- [FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers](https://mlsanity.com/paper/2301.03319/) (2023)
- [Multimodal Punctuation Prediction with Contextual Dropout](https://mlsanity.com/paper/2102.11012/) (2021)
- [Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network](https://mlsanity.com/paper/2108.11943/) (2021)
- [Vietnamese Capitalization and Punctuation Recovery Models](https://mlsanity.com/paper/2207.01312/) (2022)
- [Text Injection for Capitalization and Turn-Taking Prediction in Speech Models](https://mlsanity.com/paper/2308.07395/) (2023)
- [End-to-end Joint Punctuated and Normalized ASR with a Limited Amount of Punctuated Training Data](https://mlsanity.com/paper/2311.17741/) (2023)
- [A light-weight and efficient punctuation and word casing prediction model for on-device streaming ASR](https://mlsanity.com/paper/2407.13142/) (2024)
- [Punctuation Prediction for Polish Texts using Transformers](https://mlsanity.com/paper/2410.04621/) (2024)
- [A Proactive EMR Assistant for Doctor-Patient Dialogue: Streaming ASR, Belief Stabilization, and Preliminary Controlled Evaluation](https://mlsanity.com/paper/2604.13059/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) systems in the medical domain that focus on transcribing clinical dictations and doctor-patient conversations often pose many challenges due to the complexity of the domain. ASR output typically undergoes automatic punctuation to enable users to speak naturally, without having to vocalise awkward and explicit punctuation commands, such as "period", "add comma" or "exclamation point", while truecasing enhances user readability and improves the performance of downstream NLP tasks. This paper proposes a conditional joint modeling framework for prediction of punctuation and truecasing using pretrained masked language models such as BERT, BioBERT and RoBERTa. We also present techniques for domain and task specific adaptation by fine-tuning masked language models with medical domain data. Finally, we improve the robustness of the model against common errors made in ASR by performing data augmentation. Experiments performed on dictation and conversational style corpora show that our proposed model achieves ~5% absolute improvement on ground truth text and ~10% improvement on ASR outputs over baseline models under F1 metric.