Development of Hausa dataset a baseline for speech recognition

Ibrahim, Umar Adam; Boukar, Moussa Mahamat; Suleiman, Muhammed Aliyu

doi:10.1016/j.dib.2022.107820

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The use of datasets from online social media platforms has been explored for a variety of purposes, such as gauging people's opinions [43,44], identifying topics [45,46], detecting sexist terms [47], recognising hateful speech [48], classifying text [49], and recognising entities [50]. Previous studies have also generated relevant corpora in the Hausa language for various tasks [51,52,53,25,54,55,56]. A large collection of tweets in Hausa, Igbo, Yoruba, and Nigerian Pidgin have been compiled to improve sentiment lexicons in low-resource languages [53].…”

Section: Downstream Tasks In Hausa Languagementioning

confidence: 99%

Detection and Analysis of Offensive Online Content in Hausa Language

Adam,

Zandam,

Inuwa-Dutse

2024

Preprint

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Hausa, a major Chadic language spoken by over 100 million people in Africa, faces a challenge in the digital age. While widely used, it is considered a low-resource language from a computational linguistic perspective. This means there are limited resources and tools to analyse Hausa text, making it difficult to detect offensive and threatening language online. Our study aimed to bridge this gap. We conducted two user studies (n = 180) to understand cyberbullying in Hausa. We then created the first-ever dataset of offensive and threatening Hausa phrases to train detection systems. We developed a system to flag such content and compared it to Google translation’s ability to detect these terms. Our findings revealed a concerning trend: offensive and threatening language is prevalent online, especially in discussions about religion and politics. Our detection system was able to detect more than 70% of offensive and threatening content, although many of these were mistranslated by Google’s translation engine. We attribute this to the subtle relationship between offensive and threatening content and idiomatic expressions in the Hausa language. This highlights the importance of considering cultural nuances and idiomatic expressions in Hausa. To create a safer online environment for Hausa speakers, we recommend involving diverse stakeholders who understand local contexts and demographics. This will allow for the development of more accurate detection systems and targeted moderation strategies. Trigger Warning: Readers may find some of the terms in this study distressing or disturbing; all examples are for illustration only.

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Section: Downstream Tasks In Hausa Languagementioning

confidence: 99%

Detection and Analysis of Offensive Online Content in Hausa Language

Adam,

Zandam,

Inuwa-Dutse

2024

Preprint

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Section: Downstream Tasks In Hausa Languagementioning

confidence: 99%

Taming Toxic Talk: Detecting Offensive Online Content in Hausa

Adam,

Zandam,

Inuwa-Dutse

2024

Preprint

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Hausa, a major Chadic language spoken by over 100 million people in Africa, faces a challenge in the digital age. While widely used, it is considered a low-resource language from a computational linguistic perspective. This means there are limited resources and tools to analyse Hausa text, making it difficult to detect offensive and threatening language online. Our study aimed to bridge this gap. We conducted two user studies ($n=180$) to understand cyberbullying in Hausa. We then created the first-ever dataset of offensive and threatening Hausa phrases to train detection systems. We developed a system to flag such content and compared it to Google translation's ability to detect these terms. Our findings revealed a concerning trend: offensive and threatening language is prevalent online, especially in discussions about religion and politics. Our detection system was able to detect more than 70\% of offensive and threatening content, although many of these were mistranslated by Google's translation engine. We attribute this to the subtle relationship between offensive and threatening content and idiomatic expressions in the Hausa language. This highlights the importance of considering cultural nuances and idiomatic expressions in Hausa. To create a safer online environment for Hausa speakers, we recommend involving diverse stakeholders who understand local contexts and demographics. This will allow for the development of more accurate detection systems and targeted moderation strategies. Trigger Warning: Readers may find some of the terms in this study distressing or disturbing; all examples are for illustration only.

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“…These techniques have been applied in the low-resource scenario with some encouraging results [5,6,7,8], and pre-training has also been shown to help with accented speech [9]. However, efforts to develop ASR for African languages have so far mostly focused on data collection and model training for single languages or a small number of related or similar languages, including Amharic [10,11]; Dinka [11]; South African languages [12,13,14]; Nigerian English [15]; Hausa [16,17]; Yoruba [18,19,20,21]; Swahili [11,22,23]; Wolof [24]; Somali [25]; Igbo & Fon [26]; Bemba [27] and Akan [28].…”

Section: Introductionmentioning

confidence: 99%

Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning

Ritchie¹,

Cheng²,

Chen³

et al. 2022

Preprint

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Almost none of the 2,000+ languages spoken in Africa have widely available automatic speech recognition systems, and the required data is also only available for a few languages. We have experimented with two techniques which may provide pathways to large vocabulary speech recognition for African languages: multilingual modeling and self-supervised learning. We gathered available open source data and collected data for 15 languages, and trained experimental models using these techniques. Our results show that pooling the small amounts of data available in multilingual end-to-end models, and pretraining on unsupervised data can help improve speech recognition quality for many African languages.

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Development of Hausa dataset a baseline for speech recognition

Cited by 4 publications

References 7 publications

Detection and Analysis of Offensive Online Content in Hausa Language

Detection and Analysis of Offensive Online Content in Hausa Language

Taming Toxic Talk: Detecting Offensive Online Content in Hausa

Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning

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