Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced Data
ABSTRACT Text‐to‐speech (TTS) models have expanded the scope of digital inclusivity by becoming a basis for assistive communication technologies for visually impaired people, facilitating language learning, and allowing for digital textual content consumption in audio form across various sectors. De...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Applied AI Letters |
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| Online Access: | https://doi.org/10.1002/ail2.117 |
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| _version_ | 1849690414730182656 |
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| author | Andrew Katumba Sulaiman Kagumire Joyce Nakatumba‐Nabende John Quinn Sudi Murindanyi |
| author_facet | Andrew Katumba Sulaiman Kagumire Joyce Nakatumba‐Nabende John Quinn Sudi Murindanyi |
| author_sort | Andrew Katumba |
| collection | DOAJ |
| description | ABSTRACT Text‐to‐speech (TTS) models have expanded the scope of digital inclusivity by becoming a basis for assistive communication technologies for visually impaired people, facilitating language learning, and allowing for digital textual content consumption in audio form across various sectors. Despite these benefits, the full potential of TTS models is often not realized for the majority of low‐resourced African languages because they have traditionally required large amounts of high‐quality single‐speaker recordings, which are financially costly and time‐consuming to obtain. In this paper, we demonstrate that crowdsourced recordings can help overcome the lack of single‐speaker data by compensating with data from other speakers of similar intonation (how the voice rises and falls in speech). We fine‐tuned an English variational inference with adversarial learning for an end‐to‐end text‐to‐speech (VITS) model on over 10 h of speech from six female common voice (CV) speech data speakers for Luganda and Kiswahili. A human mean opinion score evaluation on 100 test sentences shows that the model trained on six speakers sounds more natural than the benchmark models trained on two speakers and a single speaker for both languages. In addition to careful data curation, this approach shows promise for advancing speech synthesis in the context of low‐resourced African languages. Our final models for Luganda and Kiswahili are available at https://huggingface.co/marconilab/VITS‐commonvoice‐females. |
| format | Article |
| id | doaj-art-3e5a42c2cad848daaa23adbf7f6f2158 |
| institution | DOAJ |
| issn | 2689-5595 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied AI Letters |
| spelling | doaj-art-3e5a42c2cad848daaa23adbf7f6f21582025-08-20T03:21:19ZengWileyApplied AI Letters2689-55952025-04-0162n/an/a10.1002/ail2.117Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced DataAndrew Katumba0Sulaiman Kagumire1Joyce Nakatumba‐Nabende2John Quinn3Sudi Murindanyi4Department of Electrical and Computer Engineering Makerere University Kampala UgandaDepartment of Electrical and Computer Engineering Makerere University Kampala UgandaDepartment of Computer Science Makerere University Kampala UgandaDepartment of Computer Science Makerere University Kampala UgandaDepartment of Electrical and Computer Engineering Makerere University Kampala UgandaABSTRACT Text‐to‐speech (TTS) models have expanded the scope of digital inclusivity by becoming a basis for assistive communication technologies for visually impaired people, facilitating language learning, and allowing for digital textual content consumption in audio form across various sectors. Despite these benefits, the full potential of TTS models is often not realized for the majority of low‐resourced African languages because they have traditionally required large amounts of high‐quality single‐speaker recordings, which are financially costly and time‐consuming to obtain. In this paper, we demonstrate that crowdsourced recordings can help overcome the lack of single‐speaker data by compensating with data from other speakers of similar intonation (how the voice rises and falls in speech). We fine‐tuned an English variational inference with adversarial learning for an end‐to‐end text‐to‐speech (VITS) model on over 10 h of speech from six female common voice (CV) speech data speakers for Luganda and Kiswahili. A human mean opinion score evaluation on 100 test sentences shows that the model trained on six speakers sounds more natural than the benchmark models trained on two speakers and a single speaker for both languages. In addition to careful data curation, this approach shows promise for advancing speech synthesis in the context of low‐resourced African languages. Our final models for Luganda and Kiswahili are available at https://huggingface.co/marconilab/VITS‐commonvoice‐females.https://doi.org/10.1002/ail2.117common voicecrowdsourcedKiswahililow‐resourcedLugandatext‐to‐speech |
| spellingShingle | Andrew Katumba Sulaiman Kagumire Joyce Nakatumba‐Nabende John Quinn Sudi Murindanyi Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced Data Applied AI Letters common voice crowdsourced Kiswahili low‐resourced Luganda text‐to‐speech |
| title | Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced Data |
| title_full | Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced Data |
| title_fullStr | Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced Data |
| title_full_unstemmed | Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced Data |
| title_short | Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced Data |
| title_sort | building text to speech models for low resourced languages from crowdsourced data |
| topic | common voice crowdsourced Kiswahili low‐resourced Luganda text‐to‐speech |
| url | https://doi.org/10.1002/ail2.117 |
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