Marcely Zanon Boito, Fethi Bougares, Florentin Barbier, Souhir Gahbiche, Loïc Barrault, Mickael Rouvier, Yannick Estève
| Challenge: | In this paper, we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger . we share unlabeled audio data in five languages: french, Fulfulde, Hausa, Tamaheq and Zarma . |
| Approach: | They present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. |
| Outcome: | The proposed datasets are used in the IWSLT 2022 low-resource speech translation track . they consist of radio recordings from daily broadcast news in Niger and Mali . |
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AbdelRahim A. Elmadany, Sang Yun Kwon, Hawau Olamide Toyin, Alcides Alcoba Inciarte, Hanan Aldarmaki, Muhammad Abdul-Mageed
| Challenge: | linguistic diversity in Africa is underrepresented in speech technologies, creating barriers to digital inclusion. |
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Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut (2022.lrec-1)
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| Challenge: | Using acoustic data, we develop automatic speech recognition systems for three low resource languages. |
| Approach: | They develop automatic speech recognition systems for three low resource languages using acoustic training data from 12 different languages in the hybrid DNN/HMM framework. |
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Feriji: A French-Zarma Parallel Corpus, Glossary & Translator (2024.acl-srw)
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| Challenge: | MT has seen significant advances in recent years, but the representation of African languages in MT systems is underrepresented due to linguistic complexities and limited resources. |
| Approach: | They propose a first robust parallel French-Zarma corpus and a glossary for MT that contains 61,085 sentences in Zarma and 42,789 in French. |
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The SADID Evaluation Datasets for Low-Resource Spoken Language Machine Translation of Arabic Dialects (2020.coling-main)
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| Challenge: | Low-resource Machine Translation (LRT) models are still lagging behind on low-resourced language pairs due to the scarcity of parallel training data. |
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Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi (2020.lrec-1)
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| Challenge: | a recent study shows that word embeddings can be useful for training downstream natural language processing tasks. |
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BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages (2024.lrec-main)
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Roberts Dargis, Arturs Znotins, Ilze Auzina, Baiba Saulite, Sanita Reinsone, Raivis Dejus, Antra Klavinska, Normunds Gruzitis
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Requirements and Motivations of Low-Resource Speech Synthesis for Language Revitalization (2022.acl-long)
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| Challenge: | Existing research on speech synthesis systems for three Indigenous languages in Canada requires tens of hours of audio recordings to be trained. |
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An (unhelpful) guide to selecting the best ASR architecture for your under-resourced language (2023.acl-short)
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| Challenge: | English ASR now has word error rates comparable to that of human transcriptionists, but only for the handful of the world's 7000 languages with abundant training resources. |
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TaTA: A Multilingual Table-to-Text Dataset for African Languages (2023.findings-emnlp)
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Sebastian Gehrmann, Sebastian Ruder, Vitaly Nikolaev, Jan Botha, Michael Chavinda, Ankur Parikh, Clara Rivera
| Challenge: | Existing data-to-text generation datasets are limited to English and a small number of other languages. |
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ÌròyìnSpeech: A Multi-purpose Yorùbá Speech Corpus (2024.lrec-main)
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| Challenge: | rynSpeech corpus is a dataset that can be used for both Text-to-Speecher (TTS) and Automatic Speech Recognition (ASR) speakers of many African languages have no access to voice-enabled applications in their native languages. |
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