Challenge: Using a dataset of high-quality audio, the authors examine the accents of 120 volunteers in the British Isles.
Approach: They present a dataset of high-quality audio of English sentences recorded by volunteers with different accents of the British Isles.
Outcome: The transcribed audio includes pronunciations of global locations, major airlines and common personal names in different accents.

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Writing System and Speaker Metadata for 2,800+ Language Varieties (2022.lrec-1)

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Challenge: Currently, language technologies are easily available in only a small minority of the world's 7,000+ language varieties.
Approach: They propose to use an open-source dataset to provide the writing system(s) for each of the 2,800+ languages used in the world today and an estimated speaker count for each.
Outcome: The dataset provides the attested writing system(s) for each of these 2,800+ varieties, as well as an estimated speaker count for each variety.
Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems (2020.lrec-1)

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Challenge: We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu . the datasets are primarily intended for use in text-to-speech applications, such as constructing multilingual voices or language adaptation.
Approach: They present a free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu . they use it to build a multilingual text-to-speech model that can be scaled to other languages of interest.
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The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)

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Challenge: Until recently, language descriptions were available in paper form only, with indexes as the only search aid.
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AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition (2020.lrec-1)

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Challenge: aaron e. sanchez and joe saunders: automatic speech recognition still faces a major challenge . they say accents are a way of pronouncing a language, and speakers always have manner of speaking . esassen: accents can be used to identify non-native speakers of a speech .
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A Multilingual Parallel Corpus for Aromanian (2024.lrec-main)

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Challenge: Aromanian is an endangered 1 language that currently lacks corpora and electronic resources that can potentially contribute to the preservation of its cultural heritage.
Approach: They propose to create a corpus of Aromanian and equivalent sentence-aligned translations into Romanian, English, and French using orthographic standards.
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Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs (2025.acl-long)

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Challenge: Current Large Language Models (LLMs) are predominantly designed with English as the primary language, but many are still English-dominated.
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Praaline: An Open-Source System for Managing, Annotating, Visualising and Analysing Speech Corpora (P18-4)

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Challenge: Praaline is an open-source software system for constituting and managing spoken language and multimodal corpora.
Approach: They present the latest developments of Praaline, an open-source software system for constituting and managing spoken language and multimodal corpora.
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)

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Challenge: Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models.
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Corpus Creation and Automatic Alignment of Historical Dutch Dialect Speech (2024.lrec-main)

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Challenge: The Dutch Dialect Database contains dialectal variations of Dutch recorded in the second half of the twentieth century.
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CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus (2020.lrec-1)

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Challenge: Existing datasets involve language pairs with English as source language, are low resource or lack labeled data.
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