@inproceedings{keith-2024-work,
    title = "Work in Progress: Text-to-speech on Edge Devices for Te Reo {M}{\=a}ori and {`}{\=O}lelo HawaiÊ»i",
    author = "Keith, T{\=u}reiti",
    editor = "Melero, Maite  and
      Sakti, Sakriani  and
      Soria, Claudia",
    booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.sigul-1.50",
    pages = "421--426",
    abstract = "Existing popular text-to-speech technologies focus on large models requiring a large corpus of recorded speech to train. The resulting models are typically run on high-resource servers where users synthesise speech from a client device requiring constant connectivity. For speakers of low-resource languages living in remote areas, this approach does not work. Corpora are typically small and synthesis needs to run on an unconnected, battery or solar-powered edge device. In this paper, we demonstrate how knowledge transfer and adversarial training can be used to create efficient models capable of running on edge devices using a corpus of only several hours. We apply these concepts to create a voice synthesiser for te reo M{\=a}ori (the indigenous language of Aotearoa New Zealand) for a non-speaking user and {`}{\=o}lelo HawaiÊ»i (the indigenous language of HawaiÊ»i) for a legally blind user, thus creating the first high-quality text-to-speech tools for these endangered, central-eastern Polynesian languages capable of running on a low powered edge device.",
}

Existing popular text-to-speech technologies focus on large models requiring a large corpus of recorded speech to train. The resulting models are typically run on high-resource servers where users synthesise speech from a client device requiring constant connectivity. For speakers of low-resource languages living in remote areas, this approach does not work. Corpora are typically small and synthesis needs to run on an unconnected, battery or solar-powered edge device. In this paper, we demonstrate how knowledge transfer and adversarial training can be used to create efficient models capable of running on edge devices using a corpus of only several hours. We apply these concepts to create a voice synthesiser for te reo M{\=a}ori (the indigenous language of Aotearoa New Zealand) for a non-speaking user and {`}{\=o}lelo HawaiÊ»i (the indigenous language of HawaiÊ»i) for a legally blind user, thus creating the first high-quality text-to-speech tools for these endangered, central-eastern Polynesian languages capable of running on a low powered edge device.
Keith, T{\=u}reiti
Work in Progress: Text-to-speech on Edge Devices for Te Reo Māori and `Ōlelo HawaiÊ»i