| Challenge: | Disfluencies can be introduced in conversational speech due to the conversational nature of speech and/or speech impairments such as stuttering. |
| Approach: | They propose an adversarial sequence-tagging model for Disfluency Correction . they evaluate it in Bengali, Hindi, and Marathi languages and use it to correct stuttering disfluencies . |
| Outcome: | The proposed technique improves in Bengali, Hindi, and Marathi languages . it also removes stuttering disfluencies in ASR transcripts introduced by speech impairments . |
Similar Papers
Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Spontaneous speech is rarely fluent, and disfluencies can degrade readability and reliability . a sequence tagger first marks disfluent tokens, and these signals guide instruction fine-tuning . |
| Approach: | They propose a multilingual correction pipeline where a sequence tagger first marks disfluent tokens . they add a contrastive learning objective that penalizes the reproduction of disfluency tokens. |
| Outcome: | The proposed model improves readability and reliability of ASR transcripts in three languages . disfluencies can cause misinterpretations, incoherent responses, poor user experience . |
DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European Languages (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research on disfluency correction has primarily focused on English due to the unavailability of large-scale open-source datasets. |
| Approach: | They propose to use an annotated human-annotated corpus to analyze disfluency correction in four important Indo-European languages to demonstrate the benefits. |
| Outcome: | The proposed model improves BLEU scores by 5.65 points when used with a state-of-the-art machine translation system. |
Disfluency Generation for More Robust Dialogue Systems (2023.findings-acl)
Copied to clipboard
| Challenge: | Disfluencies in user utterances can trigger a chain of errors impacting all the modules of a dialogue system. |
| Approach: | They propose to augment existing dialogue datasets with disfluent utterances by paraphrasing them into disfluente ones. |
| Outcome: | The proposed method improves dialogue state tracking and response generation by combining disfluent utterances with disfluency utteraces. |
Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech (2024.naacl-long)
Copied to clipboard
| Challenge: | Automatic speech recognition systems fail to accurately interpret speech patterns deviating from typical fluency, leading to critical usability issues and misinterpretations. |
| Approach: | They evaluate six leading automatic speech recognition systems based on a real-world dataset and a synthetic dataset derived from the widely-used LibriSpeech benchmark. |
| Outcome: | The six leading speech recognition systems were evaluated on a real-world dataset and a synthetic dataset derived from the widely-used LibriSpeech benchmark. |
DACL: Disfluency Augmented Curriculum Learning for Fluent Text Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Disfluency-aware language models are traditionally trained on fluent, written text corpora. |
| Approach: | They propose a Disfluency Augmented Curriculum Learning approach to tackle disfluency . they use CL coupled with synthetically augmented disfluent texts of various levels . |
| Outcome: | The proposed model surpasses existing techniques in word-based precision (by up to 1%) and has shown favorable recall and F1 scores. |
Fluent Translations from Disfluent Speech in End-to-End Speech Translation (N19-1)
Copied to clipboard
| Challenge: | Disfluency removal is an intermediate step between speech recognition and machine translation (MT) with the rise of end-to-end speech translation systems, disfluency recognition and removal needs to be incorporated into the model architectures or handled as a post-processing step. |
| Approach: | They propose to 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. |
| Outcome: | The proposed model generates fluent translations from disfluent speech using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. |
Robust Multilingual Part-of-Speech Tagging via Adversarial Training (N18-1)
Copied to clipboard
| Challenge: | Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations. |
| Approach: | They propose and analyze a neural POS tagging model that exploits adversarial training by training on unmodified and adversarials. |
| Outcome: | The proposed model improves overall tagging accuracy and prevents over-fitting in low resource languages and boosts tabbing accuracy for rare / unseen words. |
Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection (2022.naacl-main)
Copied to clipboard
| Challenge: | a recent study shows that current NLP models operate non-incrementally, causing unacceptable delays for the user. |
| Approach: | They propose a streaming BERT-based sequence tagging model that detects disfluencies in real-time . they train the model to decide whether to immediately output a prediction or wait for further context . |
| Outcome: | The proposed model produces accurate predictions sooner than baselines, with lower flicker . disfluencies hurt readability of ASR transcripts, erode model performance on downstream tasks . |
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering (2021.findings-acl)
Copied to clipboard
| Challenge: | Disfluencies are an under-studied topic in NLP, even though it is ubiquitous in human conversation. |
| Approach: | They propose a challenge question answering dataset where humans introduce contextual disfluencies in previously fluent questions. |
| Outcome: | The proposed dataset shows that existing models degrade significantly when tested on DISFL-QA in a zero-shot setting. |
LARD: Large-scale Artificial Disfluency Generation (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing datasets suffer from class imbalance issues, causing performance problems . Disfluency detection is a critical task in real-time dialogue systems . |
| Approach: | They propose a method for generating complex and realistic artificial disfluencies with little effort using a large-scale dataset. |
| Outcome: | The proposed method can handle repetitions, replacements, and restarts on a large-scale dataset with disfluencies. |