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

arXiv: https://arxiv.org/abs/2310.11541 · HTML: https://mlsanity.com/paper/2310.11541/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems](https://mlsanity.com/paper/1806.09514/) (2018)
- [Transfer Learning for Speech and Language Processing](https://mlsanity.com/paper/1511.06066/) (2015)
- [Exploring Transfer Learning for Low Resource Emotional TTS](https://mlsanity.com/paper/1901.04276/) (2019)
- [Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysis](https://mlsanity.com/paper/1903.11570/) (2019)
- [ASR-based Features for Emotion Recognition: A Transfer Learning Approach](https://mlsanity.com/paper/1805.09197/) (2018)
- [Language-Agnostic Syllabification with Neural Sequence Labeling](https://mlsanity.com/paper/1909.13362/) (2019)
- [Analysis and Assessment of Controllability of an Expressive Deep Learning-based TTS system](https://mlsanity.com/paper/2103.04097/) (2021)
- [Flowchase: a Mobile Application for Pronunciation Training](https://mlsanity.com/paper/2307.02051/) (2023)
- [Syllabification by Phone Categorization](https://mlsanity.com/paper/1807.05518/) (2018)

## Built on by (most cited first, 1 of 1)

- [Assessing Dutch Syllabification Algorithms and Improving Accuracy by Combining Phonetic and Orthographic Information through Deep Learning](https://mlsanity.com/paper/2605.28834/) (2026)

## Abstract (arXiv)

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 onlinefootnoteurlhttps://github.com/noetits/MUSTP-SRL that are ideal for speech representation learning, speech unit discovery, and disentanglement of speech factors in several speech-related fields.