FluentNet: End-to-End Detection of Stuttered Speech Disfluencies With Deep Learning
Abstract
Strong presentation skills are valuable and sought-after in workplace and classroom environments alike. Of the possible improvements to vocal presentations, disfluencies and stutters in particular remain one of the most common and prominent factors of someone's demonstration. Millions of people are affected by stuttering and other speech disfluencies, with the majority of the world having experienced mild stutters while communicating under stressful conditions. While there has been much research in the field of automatic speech recognition and language models, stutter detection and recognition has not received as much attention. To this end, we propose an end-to-end deep neural network, FluentNet, capable of detecting a number of different stutter types. FluentNet consists of a Squeeze-and-Excitation Residual convolutional neural network which facilitate the learning of strong spectral frame-level representations, followed by a set of bidirectional long short-term memory layers that aid in learning effective temporal relationships. Lastly, FluentNet uses an attention mechanism to focus on the important parts of speech to obtain a better performance. We perform a number of different experiments, comparisons, and ablation studies to evaluate our model. Our model achieves state-of-the-art results by outperforming other solutions in the field on the publicly available UCLASS dataset. Additionally, we present LibriStutter: a stuttered speech dataset based on the public LibriSpeech…
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LibriStutter did not collect speech from PWS but injected synthetic stuttering sounds into read speech from fluent speakers.
“LibriStutter, another influential English stuttering dataset, did not collect speech from PWS but injected synthetic stuttering sounds into read speech from fluent speakers [53].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [53]
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“LibriStutter, another influential English stuttering dataset, did not collect speech from PWS but injected synthetic stuttering sounds into read speech from fluent speakers [53].”
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“Additionally, many commonly used evaluation metrics of visual question answering systems are benchmarked against sighted preferences and do not consider BLV people’s information needs [120].”
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- Authors
- Kourkounakis, T., Hajavi, A., & Etemad, A.
- Affiliations
- Queen's University (CA)
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2021
- Volume
- 29
- Pages
- 2986-2999
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W3198298452
- Cited by
- 109
- References
- 12
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 26 · 2023: 26 · 2022: 16 · 2021: 2
- FWCI
- 6.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/ACM Transactions on Audio Speech and Language Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Social Sciences › Psychology › Clinical Psychology › Stuttering Research and Treatment
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- stuttered speech, bidirectional LSTM, attention mechanism, LibriSpeech
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