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

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 neural articulatory flow to derive highly scalable speech representations. (2) We developed a 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 in-context pronunciation learning abilities. (4) We curated Libri-Dys and open-sourced the current largest-scale co-dysfluency corpus, 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 articulatory dysfluencies. Overall, SSDM 2.0 outperforms SSDM and all other dysfluency transcription models by a large margin. See our project demo page at https://berkeley-speech-group.github.io/SSDM2.0/.

Tasks

Reproductions