# Fluent Translations from Disfluent Speech in End-to-End Speech Translation

arXiv: https://arxiv.org/abs/1906.00556 · HTML: https://mlsanity.com/paper/1906.00556/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 22 of 22)

- [FUDGE: Controlled Text Generation With Future Discriminators](https://mlsanity.com/paper/2104.05218/) (2021)
- [To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation](https://mlsanity.com/paper/2107.10821/) (2021)
- [STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation](https://mlsanity.com/paper/2203.10426/) (2022)
- [Learning Shared Semantic Space for Speech-to-Text Translation](https://mlsanity.com/paper/2105.03095/) (2021)
- [Controllable Text Generation with Neurally-Decomposed Oracle](https://mlsanity.com/paper/2205.14219/) (2022)
- [CMOT: Cross-modal Mixup via Optimal Transport for Speech Translation](https://mlsanity.com/paper/2305.14635/) (2023)
- [Understanding and Bridging the Modality Gap for Speech Translation](https://mlsanity.com/paper/2305.08706/) (2023)
- [ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/2305.14838/) (2023)
- [Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives](https://mlsanity.com/paper/2301.10761/) (2023)
- [Improving speech translation by fusing speech and text](https://mlsanity.com/paper/2305.14042/) (2023)
- [Answering Unanswered Questions through Semantic Reformulations in Spoken QA](https://mlsanity.com/paper/2305.17393/) (2023)
- [From Disfluency Detection to Intent Detection and Slot Filling](https://mlsanity.com/paper/2209.08359/) (2022)
- [He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues](https://mlsanity.com/paper/2210.15462/) (2022)
- [DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models](https://mlsanity.com/paper/2306.11825/) (2023)
- [A Representation Level Analysis of NMT Model Robustness to Grammatical Errors](https://mlsanity.com/paper/2505.21224/) (2025)
- [Z-Scores: A Metric for Linguistically Assessing Disfluency Removal](https://mlsanity.com/paper/2509.20319/) (2025)
- [Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs](https://mlsanity.com/paper/2605.12242/) (2026)
- [Toward Machine Interpreting: Lessons from Human Interpreting Studies](https://mlsanity.com/paper/2508.07964/) (2025)
- [Challenges in Translating Technical Lectures: Insights from the NPTEL](https://mlsanity.com/paper/2602.08698/) (2026)
- [Aligning Stuttered-Speech Research with End-User Needs: Scoping Review, Survey, and Guidelines](https://mlsanity.com/paper/2604.20535/) (2026)
- [Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speech](https://mlsanity.com/paper/2605.26007/) (2026)
- [The Role of Disfluencies in Speech Translation](https://mlsanity.com/paper/2608.02138/) (2026)

## Abstract (arXiv)

Spoken language translation applications for speech suffer due to conversational speech phenomena, particularly the presence of disfluencies. With the rise of end-to-end speech translation models, processing steps such as disfluency removal that were previously an intermediate step between speech recognition and machine translation need to be incorporated into model architectures. We use a sequence-to-sequence model to translate from noisy, disfluent speech to fluent text with disfluencies removed using the recently collected `copy-edited' references for the Fisher Spanish-English dataset. We are able to directly generate fluent translations and introduce considerations about how to evaluate success on this task. This work provides a baseline for a new task, the translation of conversational speech with joint removal of disfluencies.