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. |
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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. |
Integrating Disfluency-based and Prosodic Features with Acoustics in Automatic Fluency Evaluation of Spontaneous Speech (2020.lrec-1)
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| Challenge: | acoustics, prosody, and disfluency-based features are used to evaluate fluent/disfluent speech . filling pauses and word fragments are used for automatic fluency evaluation . |
| Approach: | They integrate acoustics, prosody, and disfluency-based features into an automatic fluency evaluation task. |
| Outcome: | The proposed model improves when integrated with prosodic features, but not when disfluent speech is detected. |
Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech (2024.naacl-long)
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| 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. |
End-to-End Speech Recognition and Disfluency Removal (2020.findings-emnlp)
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| Challenge: | Disfluency detection is usually an intermediate step between an automatic speech recognition system and a downstream task. |
| Approach: | They propose to train models to directly map disfluent speech into fluent transcripts without relying on a separate disfluency detection model. |
| Outcome: | The proposed models learn to generate fluent transcripts, but their performance is slightly worse than a baseline pipeline approach consisting of an ASR system and a specialized disfluency detection model. |
Adversarial Training for Low-Resource Disfluency Correction (2023.findings-acl)
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| 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 . |
Semantic Parsing of Disfluent Speech (2021.eacl-main)
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| Challenge: | Semantic parsing is a key component for understanding user utterances in voice assistants . however, most research on disfluent speech is focused on written text . |
| Approach: | They investigate semantic parsing of disfluent speech with the ATIS dataset . they add real and synthetic disfluencies at training time to improve model performance . |
| Outcome: | The proposed parser outperforms the state-of-the-art parsers on the ATIS dataset in terms of performance and accuracy. |
Using the RUPEX Multichannel Corpus in a Pilot fMRI Study on Speech Disfluencies (2020.lrec-1)
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Katerina Smirnova, Nikolay Korotaev, Yana Panikratova, Irina Lebedeva, Ekaterina Pechenkova, Olga Fedorova
| Challenge: | Numerous classifications of disfluencies have been proposed and/or implemented in annotating speech corpora. |
| Approach: | They propose to use Russian multichannel corpus RUPEX to create fragments of speech disfluencies and their clusters. |
| Outcome: | The proposed method allows to create fragments in terms of requirements for the fMRI BOLD temporal resolution. |
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 . |
Disfluency Generation for More Robust Dialogue Systems (2023.findings-acl)
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| 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. |
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. |