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ASR for Documenting Acutely Under-Resourced Indigenous Languages

Despite its potential utility for facilitating the transcription of speech recordings, automatic speech recognition (ASR) has not been widely explored as a tool for documenting endangered languages. One obstacle to adopting ASR for this purpose is that the amount of data needed to build a reliable ASR system far exceeds what would typically be available in an endangered language. Languages with highly complex morphology present further data sparsity challenges. In this paper, we present a working ASR system for Seneca, an endangered indigenous language of North America, as a case study for the development of ASR for acutely low-resource languages in need of linguistic documentation. We explore methods of leveraging linguistic knowledge to improve the ASR language models for a polysynthetic language with few high-quality audio and text resources, and we propose a tool for using ASR output to bootstrap new data to iteratively improve the acoustic model. This work serves as a proof-of-concept for speech researchers interested helping field linguists and indigenous language community members engaged in the documentation and revitalization of endangered languages.