Papers with disfluency detection
Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection (N19-1)
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| Challenge: | Disfluencies in spontaneous speech are associated with prosodic disruptions. |
| Approach: | They propose a method to extract acoustic-prosodic cues from word transcripts . they explore early and late fusion techniques for integrating text and prosody . |
| Outcome: | The proposed approach shows gains over a high-accuracy text-only model. |
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning (2020.coling-main)
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| Challenge: | Using a multi-task learning framework, we train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Approach: | They propose a multi-task learning framework to train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Outcome: | The proposed model outperforms individual tasks and delivers competitive performance. |
Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection (2020.emnlp-main)
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| Challenge: | Existing approaches to disfluency detection heavily depend on labeled data. |
| Approach: | They propose a Planner-Generator based disfluency generation model that generates natural disfluent texts as augmented data. |
| Outcome: | The proposed model outperforms baselines and leads to state-of-the-art performance on Switchboard corpus. |
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection (2020.emnlp-main)
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| Challenge: | Existing approaches to disfluency detection rely on human annotations, which are expensive to obtain. |
| Approach: | They propose an unsupervised learning paradigm which can work with unlabeled text corpora. |
| Outcome: | The proposed method performs better than existing supervised systems using word embeddings. |
Identification of Primary and Collateral Tracks in Stuttered Speech (2020.lrec-1)
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| Challenge: | Disfluency detection is a challenging task because of its different metrics depending on whether the input features are text or speech. |
| Approach: | They propose a framework for disfluency detection inspired by the clinical and the natural language processing perspective together with the theory of performance from (Clark, 1998) . they present a forced-aligned disfluence dataset and propose new audio features inspired by word-based span features. |
| Outcome: | The proposed framework outperforms baselines for speech-based predictions on a forced-aligned disfluency dataset from semi-directed interviews. |
LARD: Large-scale Artificial Disfluency Generation (2022.lrec-1)
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| 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. |
Semi-Supervised Disfluency Detection (C18-1)
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| Challenge: | Detecting disfluency can be difficult because of the flexible nature of reparandum structure and the lack of a nested structure. |
| Approach: | They propose a semi-supervised approach which extracts hidden features from self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Net (CNN). |
| Outcome: | The proposed approach improves over baselines by using unlabelled data . identifying and removing non-fluent factors would help to improve spontaneous speech quality . |
Improving Disfluency Detection by Self-Training a Self-Attentive Model (2020.acl-main)
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| Challenge: | Existing self-attentive parsers using contextualized word embeddings produce state-of-the-art results in joint parsing and disfluency detection. |
| Approach: | They propose to use contextualized word embeddings to train a neural model using unlabeled data to train parsers. |
| Outcome: | The proposed method produces state-of-the-art results in parsing and disfluency detection in speech transcripts. |
Disfluency Detection using Auto-Correlational Neural Networks (D18-1)
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| Challenge: | a recent study proposes an auto-correlational neural network (ACNN) that can detect disfluency in speech . the model uses a convolutional neural system and augments it with a new auto-corrector . |
| Approach: | They propose a convolutional neural network model that captures "rough copy" dependencies . the model is based on a new auto-correlation operator that capture the kinds of "rough copies" dependency . |
| Outcome: | The proposed model outperforms the baseline CNN on a disfluency detection task with a 5% increase in f-score. |
Adaptive Unsupervised Self-training for Disfluency Detection (2022.coling-1)
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| Challenge: | Recent studies on disfluency detection heavily relies on human annotations, which are difficult and expensive to obtain in practice. |
| Approach: | They propose an unsupervised method that reweights the importance of each training example according to its grammatical feature and prediction confidence. |
| Outcome: | The proposed method improves 2.3 points over the current SOTA unsupervised method and is competitive with the SOTA supervised method. |
Parallel Corpus for Japanese Spoken-to-Written Style Conversion (2020.lrec-1)
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| Challenge: | spoken-to-written style conversion is becoming an important technology to increase the readability of ASR transcriptions. |
| Approach: | They propose to build a Japanese parallel corpus of spoken-to-written style conversions . they use crowdsourcing to convert spoken-style text into written-style texts . |
| Outcome: | The proposed corpus can handle general and specific spoken-to-written style conversion problems in Japanese. |
Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs (2026.acl-long)
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| 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 . |