# DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction

arXiv: https://arxiv.org/abs/2305.16957 · HTML: https://mlsanity.com/paper/2305.16957/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 5 of 5)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Chunked Autoregressive GAN for Conditional Waveform Synthesis](https://mlsanity.com/paper/2110.10139/) (2021)
- [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://mlsanity.com/paper/2106.04016/) (2021)

## Abstract (arXiv)

Conversational speech often consists of deviations from the speech plan, producing disfluent utterances that affect downstream NLP tasks. Removing these disfluencies is necessary to create fluent and coherent speech. This paper presents DisfluencyFixer, a tool that performs speech-to-speech disfluency correction in English and Hindi using a pipeline of Automatic Speech Recognition (ASR), Disfluency Correction (DC) and Text-To-Speech (TTS) models. Our proposed system removes disfluencies from input speech and returns fluent speech as output along with its transcript, disfluency type and total disfluency count in source utterance, providing a one-stop destination for language learners to improve the fluency of their speech. We evaluate the performance of our tool subjectively and receive scores of 4.26, 4.29 and 4.42 out of 5 in ASR performance, DC performance and ease-of-use of the system. Our tool can be accessed openly at the following link.