Pith. sign in

REVIEW 2 cited by

XTREME-S: Evaluating Cross-lingual Speech Representations

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2203.10752 v3 pith:RC2VCJ5P submitted 2022-03-21 cs.CL

XTREME-S: Evaluating Cross-lingual Speech Representations

Alexis Conneau , Ankur Bapna , Yu Zhang , Min Ma , Patrick von Platen , Anton Lozhkov , Colin Cherry , Ye Jia
show 11 more authors
Clara Rivera Mihir Kale Daan Van Esch Vera Axelrod Simran Khanuja Jonathan H. Clark Orhan Firat Michael Auli Sebastian Ruder Jason Riesa Melvin Johnson
This is my paper
classification cs.CL
keywords speechxtreme-sbenchmarkfamiliescross-lingualdatasetslanguagesrepresentation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval. Covering 102 languages from 10+ language families, 3 different domains and 4 task families, XTREME-S aims to simplify multilingual speech representation evaluation, as well as catalyze research in "universal" speech representation learning. This paper describes the new benchmark and establishes the first speech-only and speech-text baselines using XLS-R and mSLAM on all downstream tasks. We motivate the design choices and detail how to use the benchmark. Datasets and fine-tuning scripts are made easily accessible at https://hf.co/datasets/google/xtreme_s.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. BlasBench: An Open Benchmark for Irish Speech Recognition

    cs.CL 2026-04 conditional novelty 6.0

    BlasBench supplies an Irish-aware normalizer and scoring harness that enables reproducible ASR comparisons and exposes a 33-43 point generalization gap for fine-tuned models versus 7-10 points for massively multilingual ones.

  2. GlobeAudio: A Multilingual Multicultural Benchmark for Naturalistic Evaluation of Large Audio-Language Models

    cs.CL 2026-06 unverdicted novelty 5.0

    GlobeAudio is a new multilingual multicultural benchmark for naturalistic evaluation of large audio-language models, showing performance gaps especially for open-source models and low-resource languages.