Building Text‐to‐Speech Models for Low‐Resourced Languages From Crowdsourced Data - Andrew Katumba; Sulaiman Kagumire; Joyce Nakatumba‐Nabende; John Quinn; Sudi Murindanyi (Applied AI Letters)
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.