Speech Recognition for Endangered and Extinct Samoyedic languages

Speech Recognition for Endangered and Extinct Samoyedic languages Niko Partanen University of Helsinki Finland niko.partanen@helsinki.fi Mika Hämäläinen University of Helsinki and Rootroo Ltd Finland mika@rootroo.com Tiina Klooster Luua Forestry School Estonia tiinaklooster@gmail.com Abstract Our study presents a series of experiments on speech recognition with endangered and ex- tinct Samoyedic languages, spoken in North- ern and Southern Siberia. To best of our knowledge, this is the first time a functional ASR system is built for an extinct language. We achieve with Kamas language a Label Er- ror Rate of 15%, and conclude through care- ful error analysis that this quality is already very useful as a starting point for refined hu- man transcriptions. Our results with related Nganasan language are more modest, with best model having the error rate of 33%. We show, however, through experiments where Kamas training data is enlarged incrementally, that Nganasan results are in line with what is ex- pected under low-resource circumstances of the language. Based on this, we provide rec- ommendations for scenarios in which further language documentation or archive process- ing activities could benefit from modern ASR technology. All training data and processing scripts haven been published on Zenodo with clear licences to ensure further work in this im- portant topic. 1 Introduction Samoyedic languages are spoken in the Western Siberia, and Tundra Nenets also extends far to the European part of Northern Russia. These languages belong to the Uralic language family. All currently spoken Samoyedic languages are endangered (see Moseley (2010). Tundra Nenets is the largest lan- guage in the family, while some, such as Kamas, are extinct. Even extinct Samoyedic languages have, however, been documented to various degrees. Documentation of Samoyedic languages has reached a mature stage in last decades. Three lan- guages in this group have a recent monograph long grammars in English. These are Tundra Nenets (Nikolaeva, 2014), Forest Enets (Siegl, 2013) and Nganasan (Wagner-Nagy, 2018). Similarly re- sources on these languages have been steadily be- coming available for researchers, often in connec- tion with major language documentation projects, such as ‘The word stock of the Selkup language as the main source of cultural and historical informa- tion about a moribund endangered native ethnicum of Western Siberia’1, ‘Enets and Forest Nenets’2, ‘Corpus of Nganasan Folklore Texts’3, ‘Documen- tation of Enets: digitization and analysis of legacy field materials and fieldwork with last speakers’4, ‘Tundra Nenets Texts’5, ‘Comprehensive Documen- tation and Analysis of Two Endangered Siberian Languages: Eastern Khanty and Southern Selkup’6, ‘Selkup Language Corpus (SLC)’ (Budzisch et al., 2019), ‘Nganasan Spoken Language Corpus’ (Bryk- ina et al., 2018), ‘INEL Selkup Corpus’ (Brykina et al., 2020), ‘INEL Kamas corpus’ (Gusev et al., 2019). Also Online Dictionary for Uralic Languages (Rueter and Hämäläinen, 2017) includes Tundra Nenets lexical material. 1https://cordis.europa.eu/project/id/ INTAS2005-1000006-8411 2https://dobes.mpi.nl/projects/nenets/ 3https://iling-ran.ru/gusev/Nganasan/texts/ 4https://elar.soas.ac.uk/Collection/MPI950079 5https://elar.soas.ac.uk/Collection/MPI120925 6https://elar.soas.ac.uk/Collection/MPI43298 Our study here focuses on two of these languages: Nganasan and Kamas. Our topic is ASR, but we an- chor this work into a wider context of computational resources and language documentation. The work has two goals: …

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