Transcribing speech for primarily oral, local languages is often a joint effort involving speakers and outsiders. It is commonly motivated by externally-defined scientific goals, alongside local motivations such as language acquisition and access to heritage materials. We explore the task of ‘learning through transcription’ through the design of a system for collaborative speech annotation. We have developed a prototype to support local and remote learner-speaker interactions in remote Aboriginal communities in northern Australia. We show that situated systems design for inclusive non-expert practice is a promising new direction for working with speakers of local languages.

Transcribing low resource languages can be challenging in the absence of a good lexicon and trained transcribers. Accordingly, we seek a way to enable interactive transcription whereby the machine amplifies human efforts. This paper presents a data model and a system architecture for interactive transcription, supporting multiple modes of interactivity, increasing the likelihood of finding tasks that engage local participation in language work. The approach also supports other applications which are useful in our context, including spoken document retrieval and language learning.