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Dataset . 2022
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition

Authors: Mukiibi, Jonathan; Hussein, Ali; Meyer, Joshua; Katumba, Andrew; Nakatumba-Nabende, Joyce;

The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition

Abstract

The Makerere AI Lab has built an end-to-end CTC Luganda ASR model using radio data. Having encountered data challenges in working with low resource languages, we take the initiative together with our partners to release the first radio corpus for Luganda. The corpus of 155 hours is publicly available online under the Creative Commons BY-NC-ND 4.0 license. The dataset release is comprised of the following: 20 hours of human transcribed radio speech. The audio is 16kHZ, mono channel and with 16 bit rate. Two CSV files for the 20-hour human transcribed dataset - cleaned.csv contains cleaned transcripts and uncleaned.csv contains uncleaned transcripts. The uncleaned transcripts contain extra speech details included in tags like [laughter] for laughter, and [um] for filler pauses, which speaker is talking, where each speaker is assigned an identifier A or B. The transcription guide used to transcribe the radio dataset. A multi-speaker untranscribed dataset of 6 hours of radio data. 1.4 hours of women voices and 4.6 hours of men voices. Each audio is a ten-seconds clip with a single speaker. 135 hours of multi-speaker untranscribed radio data. NOTE: You can read and cite our paper published in the Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022) The Dataset is published under Creative Commons BY-NC-ND 4.0 license and in order for us to monitor who is using it for the right license we request that you reach out to us officially. 

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Keywords

Luganda , radio speech corpus, automatic speech recognition

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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