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In this paper, we present a methodology for linguistic feature extraction,focusing particularly on automatically syllabifying words in multiplelanguages, with a design to be compatible with a forced-alignment tool, theMontreal Forced Aligner (MFA). In both the textual and phonetic domains, ourmethod focuses on the extraction of phonetic transcriptions from text, stressmarks, and a unified automatic syllabification (in text and phonetic domains).The system was built with open-source components and resources. Through anablation study, we demonstrate the efficacy of our approach in automaticallysyllabifying words from several languages (English, French and Spanish).Additionally, we apply the technique to the transcriptions of the CMU ARCTICdataset, generating valuable annotations availableonline\footnote{\url{https://github.com/noetits/MUST_P-SRL}} that are ideal forspeech representation learning, speech unit discovery, and disentanglement ofspeech factors in several speech-related fields.

MUST&P-SRL: Multi-lingual and Unified Syllabification in Text and Phonetic Domains for Speech Representation Learning