Fashioning Local Designs from Generic Speech Technologies in an A ustralian Aboriginal Community (2022)

Abstract: An increasing number of papers have been addressing issues related to low-resource languages and the transcription bottleneck paradigm. After several years spent in Northern Australia, where some of the strongest Aboriginal languages are spoken, we could observe a gap between the motivations depicted in research contributions in this space and the Northern Australian context. In this paper, we address this gap in research by exploring the potential of speech recognition in an Aboriginal community. We describe our work from training a spoken term detection system to its implementation in an activity with Aboriginal participants. We report here on one side how speech recognition technologies can find their place in an Aboriginal context and, on the other, methodological paths that allowed us to reach better comprehension and engagement from Aboriginal participants.

## introduction a consistent theme in recent nlp research has been doing more with less @xcite @xcite @xcite . it is popular to describe new pipelines to solve a wide range of tasks for under-resourced languages @xcite @xcite @xcite . however, the motivations behind the design of a computational method are not systematically well justified according to the needs of the target speech communities. the category of under-resourced languages encompasses a wide range of contexts, not simply in terms of the quantity of data available but also in terms of local speech communities' sociolinguistic and political situation @xcite . often, the focus has been to generalise a given method across languages, where the proposed system is at the core of the argument instead of the benefits that it could have for the speakers. we could ask whether the same language technology would be equally applicable to marathi, spoken by millions in a major metropolis, and miriwoong, with only a few elderly speakers in a remote australian aboriginal community (cf. @xcite . universal solutions dominate nlp: research and results are often provided without taking into account the global situation of the languages involved or the views of the speech communities about the preservation of their language. instead, it is common to assert that an improvement in word error rate yielded by a given speech recognition system is the answer to the transcription bottleneck and, therefore, the problem of scaling up language documentation @xcite . most of the world's languages are primarily oral @xcite . writing is often not a priority, and very few people are skilled in transcribing their language. written resources often only exist in limited spaces where there is a collaboration between westerners and local communities, such as schools, ranger programs, tourism, and academia. in such cases, writing would seem to primarily serve institutional agendas @xcite @xcite . accordingly, we must ask ourselves to what extent automatic transcription technologies have a place in research that respects local self-determination. @xcite calls for a local turn, for the need to work with local speech communities from the ground up. in other words, outsiders who enter communities with their expertise need to begin with local concerns and local knowledge practices, and only later begin to explore ways in which language technologies can be added into the mix. for example, a local person might want non-indigenous colleagues to learn and use the local language, rather than assuming that all work is conducted in english. we have found that such an approach enlarges the opportunities for collaboration, while simultaneously generating language resources. this paper extends our previous work on collaborative transcription @xcite , where the language documentation pipeline we designed failed. we were confronted with different ways of knowing and different expectations in terms of language work. in this work, learning from our past failure, we describe our approach, from the training of a transcription system to the design of collaborative transcription activities with aboriginal participants. we first describe our speech recognition method based on syllable spotting. we then present the design of the app used that bridges the output of the syllable spotting system to the people, taking into account existing practices. we also explain our method to engage with participants to address their interests in terms of language work. finally, we detail the application of the proposed transcription activities and discuss the success and flaws of this work. ## conclusion generic speech recognition methods for underresourced languages offer the potential to support small speech communities. yet the translation of such methods into community-based projects is rare. we have presented a study on the creation and testing of a syllable spotting-based transcription interface to enable the creation of written resources by the members of an aboriginal community in the australian top end. based on the challenges encountered in previous work, we went from word spotting to syllable spotting to reach a denser transcription and enabled a transcription method closer to existing practices. with the help of collaborators, we designed a transcription interface that allowed the users to build the transcription of given audio using the syllable spotted by our system. we reported the testing of the app with three participants at different stages of development, including lessons learnt from their interaction with the transcription activity and the app design. research guidelines push scholars to decolonise their practices and to go towards self-determination. yet the translation of guidelines to real-life applications is unclear, specifically in cross-cultural collaborations with different ways of knowing. this work allowed us to highlight methodological paths that improved the engagement and comprehension of the participants. the activity sheet, for instance, made clear that the activity was about creating a wordlist which was not necessarily clear based on our explanation. dividing the activity between an elicitation part and a transcription part allowed us to hook the interest of the participants with a task they were familiar with and allowed us to clarify the context of our work in contrast to the sparse transcription of random sentences explored previously @xcite . all participants frequently used the playback of the syllables in isolation and their concatenation, confirming its engaging aspect. documentary linguistics has often been undertaken by non-indigenous linguists where the collaboration with the community did not go further than the collection of spoken data (first languages australia, 2014). in this work, we initially wanted to counterbalance these practices by enabling community-based language documentation. yet keeping a language strong does not need to be about language documentation, and bininj people who took part in this work did not seem to buy into documentary linguistics practices. instead, they seemed to see the interface as a literacy learning tool. keeping language strong is seen as building capabilities instead of creating and storing language material. community-based implies an active role of the community in the work we conducted, and following their view in terms of language work is then crucial. the cross-cultural challenges we encountered required extra work to enable a common ground we could build on. now that comprehension issues are solved, that we have a better comprehension of people agenda and covid-19 restrictions start to be eased, more iteration can happen to allow the community to take control of the design of the proposed tool to better fit their agenda and practices.