Pith. sign in

REVIEW 2 cited by

The Multilingual TEDx Corpus for Speech Recognition and Translation

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 2102.01757 v2 pith:57WD6E7Y submitted 2021-02-02 cs.CL

Elizabeth Salesky , Matthew Wiesner , Jacob Bremerman , Roldano Cattoni , Matteo Negri , Marco Turchi , Douglas W. Oard , Matt Post This is my paper · ORCID
classification cs.CL
keywords corpuslanguagesmultilingualspeechtedxtranslationaudiorecognition
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present the Multilingual TEDx corpus, built to support speech recognition (ASR) and speech translation (ST) research across many non-English source languages. The corpus is a collection of audio recordings from TEDx talks in 8 source languages. We segment transcripts into sentences and align them to the source-language audio and target-language translations. The corpus is released along with open-sourced code enabling extension to new talks and languages as they become available. Our corpus creation methodology can be applied to more languages than previous work, and creates multi-way parallel evaluation sets. We provide baselines in multiple ASR and ST settings, including multilingual models to improve translation performance for low-resource language pairs.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. Unified Semi-Supervised Pipeline for Automatic Speech Recognition

    eess.AS 2025-06 conditional novelty 5.0 of 10

    A new semi-supervised ASR framework, TopIPL, combines a dynamic pseudo-label cache and top-N checkpoint teacher averaging to improve WER by up to 40 percent in low-resource settings.

  2. ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A three-stage iterative LoRA training recipe (Focus, Feed Back, Fix) is applied to Whisper-large-v3 and Qwen2-Audio, reporting WER reductions on a multilingual ASR benchmark, with the gains attributed to the iterative...

Pith tools