| Challenge: | Existing speech recognition systems for African languages are very low . lack of resources (speech and text) can be attributed to poor quality of speech. |
| Approach: | They present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting of 24 hours of read speech in the Bemba language. |
| Outcome: | The proposed model achieves a word error rate (WER) of 32.91% on the Bemba language . the 1 billion XLS-R parameter model achieve better performance than the monolingual pre-trained English model on the corpus. |
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BIG-C: a Multimodal Multi-Purpose Dataset for Bemba (2023.acl-long)
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| Challenge: | Existing work in the area of radio browsing using automatic speech recognition (ASR) has been done by the United Nations in Uganda, and Keyword Spotting systems in Somalia. |
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Solomon Teferra Abate, Martha Yifiru Tachbelie, Michael Melese, Hafte Abera, Tewodros Abebe, Wondwossen Mulugeta, Yaregal Assabie, Million Meshesha, Solomon Afnafu, Binyam Ephrem Seyoum
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Rolando Coto-Solano, Sally Akevai Nicholas, Samiha Datta, Victoria Quint, Piripi Wills, Emma Ngakuravaru Powell, Liam Koka’ua, Syed Tanveer, Isaac Feldman
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Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation (L18-1)
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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen
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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. |
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From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition (2025.emnlp-main)
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| Challenge: | Recent advances in Automatic Speech Recognition (ASR) have been fueled by massive speech corpora, but extending coverage to diverse languages with limited resources remains a formidable challenge. |
| Approach: | They propose a pipeline that converts large-scale text corpora into synthetic speech using off-the-shelf text-to-speech (TTS) models. |
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A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
| Approach: | They propose to use unlabeled speech data to build multilingual ASR models that can be used for improved performance on low-resource languages. |
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