MUST&P-SRL: Multi-lingual and Unified Syllabification in Text and
  Phonetic Domains for Speech Representation Learning
Noé Tits
In this paper, we present a methodology for linguistic feature extraction,
focusing particularly on automatically syllabifying words in multiple
languages, with a design to be compatible with a forced-alignment tool, the
Montreal Forced Aligner (MFA). In both the textual and phonetic domains, our
method focuses on the extraction of phonetic transcriptions from text, stress
marks, and a unified automatic syllabification (in text and phonetic domains).
The system was built with open-source components and resources. Through an
ablation study, we demonstrate the efficacy of our approach in automatically
syllabifying words from several languages (English, French and Spanish).
Additionally, we apply the technique to the transcriptions of the CMU ARCTIC
dataset, generating valuable annotations available
online\footnote{\url{https://github.com/noetits/MUST_P-SRL}} that are ideal for
speech representation learning, speech unit discovery, and disentanglement of
speech factors in several speech-related fields.