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.
@InProceedings{JIMERSON18.749, author = {Robert Jimerson and Emily Prud'hommeaux}, title = "{ASR for Documenting Acutely Under-Resourced Indigenous Languages}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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