mlsanityData to 7 Oct 2026

SSDM: Scalable Speech Dysfluency Modeling

arXiv 2408.16221 ↗First posted 29 Aug 2024eess.AS cs.AI cs.CLcited by 13

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Abstract (arXiv)

Speech dysfluency modeling is the core module for spoken language learning, and speech therapy. However, there are three challenges. First, current state-of-the-art solutionscitelian2023unconstrained-udm, lian-anumanchipalli-2024-towards-hudm suffer from poor scalability. Second, there is a lack of a large-scale dysfluency corpus. Third, there is not an effective learning framework. In this paper, we propose textitSSDM: Scalable Speech Dysfluency Modeling, which (1) adopts articulatory gestures as scalable forced alignment; (2) introduces connectionist subsequence aligner (CSA) to achieve dysfluency alignment; (3) introduces a large-scale simulated dysfluency corpus called Libri-Dys; and (4) develops an end-to-end system by leveraging the power of large language models (LLMs). We expect SSDM to serve as a standard in the area of dysfluency modeling. Demo is available at urlhttps://berkeley-speech-group.github.io/SSDM/.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 85 of its 139 references that are in the map. First posted on arXiv 29 Aug 2024.

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85 of 139 references.

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38 more
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