--- id: hutchinson26_interspeech title: "Working Together on Technologies: A Case Study of Collaboration in Aotearoa" authors: - Ben Hutchinson - Gabriella Conlon - Greg Duncum - Carrie Jones - Tāne Karamaina - Hautahi Kingi - Jamie Towler - Caroline Rainsford - Ngahiwi Apanui-Barr year: 2026 doi: 10.21437/Interspeech.2026-1573 isca_url: https://www.isca-archive.org/interspeech_2026/hutchinson26_interspeech.html pdf_url: https://www.isca-archive.org/interspeech_2026/hutchinson26_interspeech.pdf session: "Pacific Voices: Speech Science and Technology for the Languages of the Pacific Ocean" topics: - tts - low-resource - multilingual category: tts labels: - low-resource - multilingual - generative-model institutions: - Google - Te Taura Whiri i te Reo Māori code: url: "" license: "" open_to_collaboration: false wiki_frontmatter: id: hutchinson26_interspeech category: tts labels: - low-resource - multilingual - generative-model institutions: - Google - Te Taura Whiri i te Reo Māori updated: 2026-09-29 confidence: full-paper digest: v2 source: https://doi.org/10.21437/Interspeech.2026-1573 pdf: https://www.isca-archive.org/interspeech_2026/hutchinson26_interspeech.pdf wiki_url: https://interspeech-2026-wiki.vercel.app/papers/hutchinson26_interspeech/ markdown_url: https://interspeech-2026-wiki.vercel.app/papers/hutchinson26_interspeech/markdown.md --- # Working Together on Technologies: A Case Study of Collaboration in Aotearoa *Ben Hutchinson, Gabriella Conlon, Greg Duncum, Carrie Jones, Tāne Karamaina, Hautahi Kingi, Jamie Towler, Caroline Rainsford, Ngahiwi Apanui-Barr* [PDF](https://www.isca-archive.org/interspeech_2026/hutchinson26_interspeech.pdf) · [ISCA page](https://www.isca-archive.org/interspeech_2026/hutchinson26_interspeech.html) · [DOI](https://doi.org/10.21437/Interspeech.2026-1573) **Category:** `tts` · **Labels:** `low-resource`, `multilingual`, `generative-model` **TL;DR** — This paper presents a collaborative case study between Te Taura Whiri i te Reo Māori and Google to develop a Text-to-Speech (TTS) voice for Aotearoa/New Zealand that fluently speaks English with a local accent and accurately pronounces te reo Māori place names. It details the cultural, epistemological, and structural challenges encountered and the co-developed frameworks used to overcome them. ## Key contributions - Co-developed an Engagement Framework structured around Māori kaupapa (manaakitanga, whakawhanaungatanga, and taunaki) to align mismatched partnership expectations. - Established an innovative Commitments And Recorded Preferences (CARP) structure within an Alignment Framework to record disagreements and ideal outcomes. - Created an authentic recording script and robust pronunciation lexicon that accommodates regional orthographic variations (e.g., macron vs. doubled vowel conventions) and mixed-language interactions. - Implemented a mixed-methods evaluation protocol combining quantitative New Zealand English ratings with qualitative, cross-organizational reviews focused on te reo Māori place name pronunciation. ## Problem Indigenous languages like te reo Māori have historically been underserved by global technology companies whose products prioritize dominant global languages and English-centric norms. Prior approaches often reduce language to decontextualized data, ignore collective Indigenous data sovereignty, and rely on top-down Western frameworks that fail to respect community guardianship. This matters because technology products like screen readers and navigation systems risk perpetuating colonial domination and corrupting linguistic treasures unless developed via respectful, participatory processes. ## Method ## Experimental setup The project involved collaboration between Te Taura Whiri i te Reo Māori and Google, leveraging a co-developed pronunciation lexicon and evaluation dataset of diverse te reo Māori place names. Evaluation utilized a panel of 10 total raters (5 from each organization) assessing real-time model outputs for te reo Māori pronunciation alongside a separate pool of New Zealand English raters for driving directions. ## Results The partnership successfully delivered a New Zealand TTS voice capable of natural English pronunciation paired with correct te reo Māori place name handling. Collaborative mixed-methods evaluations confirmed that integrating community-led lexicons and relaxed talent-anonymity protocols resolved historical pronunciation and cultural deficiencies in local speech technologies. ## Limitations The partnership framework is specific to the bicultural and historical context of Aotearoa/New Zealand and may require substantial adaptation for other Indigenous language revitalization efforts. The scope is bounded by the pragmatic constraints of corporate product deployment, where certain ideal partner preferences (such as redirecting all commercial revenue to language revitalization) could not be fully implemented. ## Why read this Speech and ML engineers building language technologies for Indigenous or minoritized communities should read this to understand how to move beyond extractive data practices and establish genuine, culturally aligned governance and co-development processes. ## Code None released (as of this page's `updated` date). If you are an author with a repo, please claim this entry — see CONTRIBUTING.md. ## Applications Text-to-speech systems, navigation guidance, screen readers, and localized voice assistants in bilingual or Indigenous language contexts. ## Institutions / 機構 Google, Te Taura Whiri i te Reo Māori ## Related - [Indigenising Speech Technology: Building a TTS Model for te Reo Māori](leoni26_interspeech.md) — same problem · relatedness 2.6/3 - [Mapping Acceptable Pronunciation Range for te reo Māori through Perceptual, Acoustic, and Marker Evaluative Data](evans26_interspeech.md) — complementary · relatedness 1.9/3 - [When Machines Speak Like Local Peers: Improving Conversational Experiences with Accent-Adaptive Voice Agents](garnaik26_interspeech.md) — same problem · relatedness 1.9/3 - [Decolonizing Linguistic Policies in Automatic Speech Recognition: A Framework for Cross-Culturally Competent Speech AI](cunningham26_interspeech.md) — complementary · relatedness 1.9/3 - [Two Lessons Learned from the SGILE project: Efficient Building and Evaluation of TTS Voices](pine26_interspeech.md) — same problem · relatedness 1.9/3 All 950k paper pairs scored by TypeSafe Jev (`scripts/related/`); relatedness 0 = unrelated … 3 = directly comparable.