mlsanityData to 7 Oct 2026

Evaluating Self-Supervised Speech Representations for Indigenous American Languages

arXiv 2310.03639 ↗First posted 5 Oct 2023cs.CL eess.AScited by 6

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Abstract (arXiv)

The application of self-supervision to speech representation learning has garnered significant interest in recent years, due to its scalability to large amounts of unlabeled data. However, much progress, both in terms of pre-training and downstream evaluation, has remained concentrated in monolingual models that only consider English. Few models consider other languages, and even fewer consider indigenous ones. In our submission to the New Language Track of the ASRU 2023 ML-SUPERB Challenge, we present an ASR corpus for Quechua, an indigenous South American Language. We benchmark the efficacy of large SSL models on Quechua, along with 6 other indigenous languages such as Guarani and Bribri, on low-resource ASR. Our results show surprisingly strong performance by state-of-the-art SSL models, showing the potential generalizability of large-scale models to real-world data.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 20 of its 46 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

20 of 46 references.

References · 20

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8 more
  1. 2020Participatory Research for Low-resourced Machine Translation: A Case Study in African Languagescited by 159
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  3. 2023ML-SUPERB: Multilingual Speech Universal PERformance Benchmarkcited by 70
  4. 2020Masakhane -- Machine Translation For Africacited by 48
  5. 2023Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Computecited by 21
  6. 2022Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translationcited by 11
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  8. 2022Huqariq: A Multilingual Speech Corpus of Native Languages of Peru for Speech Recognitioncited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 1.

6 papers.

  1. 2024ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasetscited by 26
  2. 2023Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyondcited by 10
  3. 2024Automatic Speech Recognition Advancements for Indigenous Languages of the Americascited by 1
  4. 2025MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discoverycited by 1
  5. 2025FUSE : A Ridge and Random Forest-Based Metric for Evaluating MT in Indigenous Languagescited by 0
  6. 2026Giving Voice to the Constitution: Low-Resource Text-to-Speech for Quechua and Spanish Using a Bilingual Legal Corpuscited by 0