Automatic speech recognition model development report
This project builds on previous work where an earlier study sought to establish proof of concept on how speech recognition, along with natural language processing and text analysis techniques, can help to better identify farmer demands and target digital extension, particularly in the Global South....
| Autores principales: | , , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
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CGIAR System Organization
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179531 |
| _version_ | 1855536873185738752 |
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| author | Mganga, Nelson Jones-Garcia, Eliot Koo, Jawoo |
| author_browse | Jones-Garcia, Eliot Koo, Jawoo Mganga, Nelson |
| author_facet | Mganga, Nelson Jones-Garcia, Eliot Koo, Jawoo |
| author_sort | Mganga, Nelson |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This project builds on previous work where an earlier study sought to establish proof of concept on how speech recognition, along with natural language processing and text analysis techniques, can help to better identify farmer demands and target digital extension, particularly in the Global South.
The study ultimately led to development of Longa–an automated speech recognition tool for bantu languages and the work detailed in this report aimed to extend Longa’s capabilities by improving performance of previously tested Luganda models, extending the tool’s functionality to the West African context, with a focus on the Malian Language of Bambara, as well as creating a more user-friendly interface to allow for integration and deployment in more practical settings.
This report details the development of automatic speech recognition models for Luganda and Bambara designed for integration and operation within the existing framework at Farm Radio International (FRI), i.e. the Uliza application. The report is divided into four main sections describing activities and methods utilized in processing (annotation, preparation, cleaning, and modeling) radio recordings used to train the models, finetuning and evaluation of the models, as well as setting up interfaces on Hugging Face and Google Colab for testing. |
| format | Informe técnico |
| id | CGSpace179531 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | CGIAR System Organization |
| publisherStr | CGIAR System Organization |
| record_format | dspace |
| spelling | CGSpace1795312026-01-09T02:13:55Z Automatic speech recognition model development report Mganga, Nelson Jones-Garcia, Eliot Koo, Jawoo artificial intelligence data analysis agricultural extension automation digital technology This project builds on previous work where an earlier study sought to establish proof of concept on how speech recognition, along with natural language processing and text analysis techniques, can help to better identify farmer demands and target digital extension, particularly in the Global South. The study ultimately led to development of Longa–an automated speech recognition tool for bantu languages and the work detailed in this report aimed to extend Longa’s capabilities by improving performance of previously tested Luganda models, extending the tool’s functionality to the West African context, with a focus on the Malian Language of Bambara, as well as creating a more user-friendly interface to allow for integration and deployment in more practical settings. This report details the development of automatic speech recognition models for Luganda and Bambara designed for integration and operation within the existing framework at Farm Radio International (FRI), i.e. the Uliza application. The report is divided into four main sections describing activities and methods utilized in processing (annotation, preparation, cleaning, and modeling) radio recordings used to train the models, finetuning and evaluation of the models, as well as setting up interfaces on Hugging Face and Google Colab for testing. 2025-12-31 2026-01-08T16:48:18Z 2026-01-08T16:48:18Z Report https://hdl.handle.net/10568/179531 en https://hdl.handle.net/10568/127005 Open Access application/pdf CGIAR System Organization Mganga, Nelson; Jones-Garcia, Eliot; and Koo, Jawoo. 2025. Automatic speech recognition model development report. CGIAR System Organization. https://hdl.handle.net/10568/179531 |
| spellingShingle | artificial intelligence data analysis agricultural extension automation digital technology Mganga, Nelson Jones-Garcia, Eliot Koo, Jawoo Automatic speech recognition model development report |
| title | Automatic speech recognition model development report |
| title_full | Automatic speech recognition model development report |
| title_fullStr | Automatic speech recognition model development report |
| title_full_unstemmed | Automatic speech recognition model development report |
| title_short | Automatic speech recognition model development report |
| title_sort | automatic speech recognition model development report |
| topic | artificial intelligence data analysis agricultural extension automation digital technology |
| url | https://hdl.handle.net/10568/179531 |
| work_keys_str_mv | AT mganganelson automaticspeechrecognitionmodeldevelopmentreport AT jonesgarciaeliot automaticspeechrecognitionmodeldevelopmentreport AT koojawoo automaticspeechrecognitionmodeldevelopmentreport |