To produce high-quality Natural Language Processing (NLP) technologies for low-resource languages, authentic leadership and participation from the low-resource language community is crucial. This reduces chances of bias, surveillance and the inclusion of inaccurate data that can negatively impact output in language technologies. It also ensures that decision-making throughout the pipeline of work centres on the language community rather than only prioritising metrics. The NLP building process involves a range of steps and decisions to ensure the production of successful models and outputs. Rarely does a model perform as expected or desired the first time it is deployed for testing, resulting in the need for re-assessment and re-deployment. This paper discusses the process involved in solving failure modes for a Māori language automatic speech recognition (ASR) model. It explains how the data is curated and how language and data specialists offer unparalleled insight into the debugging process because of their knowledge of the data. This expertise has a significant influence on decision-making to ensure the entire pipeline is embedded in ethical practice and the work is culturally appropriate for the Māori language community thus creating trustworthy language technology.

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 Māori (the indigenous language of Aotearoa New Zealand) for a non-speaking user and ‘ōlelo Hawaiʻi (the indigenous language of Hawaiʻi) 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.