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Angelo Ortiz Tandazo

Angelo Ortiz Tandazo has 2 papers in our library, in 2026, most of them on Speech & spoken language; often with Youssef Benchekroun, Emmanuel Dupoux, Martin Gleize.

2 papers in our library · 2026

OpenAlex · h-index 1 · 8 works · 1 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11431

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🔬École Normale Supérieure - PSLParis · France2025–2026 · 5 paperscurrent🔬Centre National de la Recherche ScientifiqueParis · France2025–2026 · 3 paperscurrent🔬École des hautes études en sciences socialesParis · France2025–2026 · 3 paperscurrent🎓Université Paris Sciences et LettresParis · France2025–2026 · 2 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksPsychology · 1 workArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 2 worksExperimental and Cognitive Psychology · 1 work

🧭 Topics 1Research subjects and languages the work is about.Speech & spoken language · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery (2026 · 1 citation)
    Self-supervised speech models such as HuBERT need very large amounts of clean audio. The units they discover are also shorter and more numerous than real phonemes, and they stay sensitive to speaker and phonetic context. This limits their use for low-resource and unwritten languages.
  2. SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (2026)
    Self-supervised speech models such as HuBERT and WavLM need thousands of hours of audio to learn useful linguistic units. Infants manage this with a few hundred hours. SpidR-Adapt aims to narrow that gap, so that speech units can be learned for a new language from very little unlabeled audio.

 Co-authors here

5 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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