Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Non-Contrastive Self-Supervised Speech Representations vs. Wav2Vec 2.0 in Low-Resource Languages

Domaine:

natural language processing

Type de record:

paper
Créateur:
Ass
Éditeur:
Zenodo
Hôte:avatar
This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How do non-contrastive self-supervised speech representations scale in terms of training efficiency and downstream classification performance compared to wav2vec 2.0 on low-resource language datasets. 12 claims were extracted from source literature; 12 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How do non-contrastive self-supervised speech representations scale in terms of training efficiency and downstream classification performance compared to wav2vec 2.0 on low-resource language datasets? Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research. Machine-generated literature synthesis. Content is derived from peer-reviewed papers; see individual sources for authoritative data. Automated review score: 9.0/10. Published by Assignee Research (assignee.net).

Visit

doi.orgzenodo.org

Tasks

speech processing

Tags

non-contrastiveself-supervisedspeechrepresentationsscaletermstrainingefficiency

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitchingCSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource LanguagesDo Discrete Self-Supervised Representations of Speech Capture Tone Distinctions?ConLID: Supervised Contrastive Learning for Low-Resource Language IdentificationFine-Tuned Self-Supervised Speech Representations for Language Diarization in Multilingual Code-Switched SpeechSelf-supervised Speech Representations Still Struggle with African American Vernacular English

Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching

While many speakers of low-resource languages regularly code-switch between their languages and othe

CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages

Neural dependency parsing has achieved remarkable performance for low resource morphologically rich

Do Discrete Self-Supervised Representations of Speech Capture Tone Distinctions?

Discrete representations of speech, obtained from Self-Supervised Learning (SSL) foundation models,

ConLID: Supervised Contrastive Learning for Low-Resource Language Identification

Language identification (LID) is a critical step in curating multilingual LLM pretraining corpora fr

Fine-Tuned Self-Supervised Speech Representations for Language Diarization in Multilingual Code-Switched Speech

Annotating a multilingual code-switched corpus is a painstaking process requiring specialist linguis

Self-supervised Speech Representations Still Struggle with African American Vernacular English

Underperformance of ASR systems for speakers of African American Vernacular English (AAVE) and other