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SSDM: Scalable Speech Dysfluency Modeling by Lian, Jiachen; Zhou, Xuanru; Ezzes, Zoe; Vonk, Jet; Morin, Brittany; Baquirin, David; Mille, Zachary; Tempini, Maria Luisa Gorno; Anumanchipalli, Gopala Krishna is a scholarly article available to read on EtoBox.

What is SSDM: Scalable Speech Dysfluency Modeling about?

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 solutions\cite{lian2023unconstrained-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 \textit{SSDM: 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 \url{https://berkeley-speech-group.github.io/SSDM/}.

Author

Lian, Jiachen; Zhou, Xuanru; Ezzes, Zoe; Vonk, Jet; Morin, Brittany; Baquirin, David; Mille, Zachary; Tempini, Maria Luisa Gorno; Anumanchipalli, Gopala Krishna

Published

2024

Language

EN