SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies

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

arXiv · 2412.00265

SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies

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

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Abstract

Speech is a hierarchical collection of text, prosody, emotions, dysfluencies, etc. Automatic transcription of speech that goes beyond text (words) is an underexplored problem. We focus on transcribing speech along with non-fluencies (dysfluencies). The current state-of-the-art pipeline SSDM suffers from complex architecture design, training complexity, and significant shortcomings in the local sequence aligner, and it does not explore in-context learning capacity. In this work, we propose SSDM 2.0, which tackles those shortcomings via four main contributions: (1) We propose a novel \textit{neural articulatory flow} to derive highly scalable speech representations. (2) We developed a \textit{full-stack connectionist subsequence aligner} that captures all types of dysfluencies. (3) We introduced a mispronunciation prompt pipeline and consistency learning module into LLM to leverage dysfluency \textit{in-context pronunciation learning} abilities. (4) We curated Libri-Dys and open-sourced the current largest-scale co-dysfluency corpus, \textit{Libri-Co-Dys}, for future research endeavors. In clinical experiments on pathological speech transcription, we tested SSDM 2.0 using nfvPPA corpus primarily characterized by \textit{articulatory dysfluencies}. Overall, SSDM 2.0 outperforms SSDM and all other dysfluency transcription models by a large margin. See our project demo page at \url{https://berkeley-speech-group.github.io/SSDM2.0/}.

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BibTeX RIS

Jiachen Lian, Xuanru Zhou, Zoe Ezzes, Jet Vonk, Brittany Morin, David Baquirin, Zachary Mille, Maria Luisa Gorno Tempini, Gopala Krishna Anumanchipalli. 2024-11-29. SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies. https://arxiv.org/abs/2412.00265

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