mlsanityData to 14 Oct 2026

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

arXiv 2310.11541 ↗First posted 17 Oct 2023cs.CL cs.AI cs.LGcited by 1

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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.

Key facts. Cited by 1 paper 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

11 of 29 references.

References · 11

  1. 2018A Survey on Deep Transfer Learningcited by 419
  2. 2021Emotional Voice Conversion: Theory, Databases and ESDcited by 115
  3. 2018The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systemscited by 61
  4. 2015Transfer Learning for Speech and Language Processingcited by 35
  5. 2019Exploring Transfer Learning for Low Resource Emotional TTScited by 21
  6. 2019Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysiscited by 8
  7. 2018ASR-based Features for Emotion Recognition: A Transfer Learning Approachcited by 6
  8. 2019Language-Agnostic Syllabification with Neural Sequence Labelingcited by 4
  9. 2021Analysis and Assessment of Controllability of an Expressive Deep Learning-based TTS systemcited by 4
  10. 2023Flowchase: a Mobile Application for Pronunciation Trainingcited by 2
  11. 2018Syllabification by Phone Categorizationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Assessing Dutch Syllabification Algorithms and Improving Accuracy by Combining Phonetic and Orthographic Information through Deep Learningcited by 0