New Baseline in Automatic Speech Recognition for Northern Sámi

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Automatic speech recognition has gone through many changes in recent years. Advances both in computer hardware and machine learning have made it possible to develop systems far more capable and complex than the previous state-of-the-art. However, almost all of these improvements have been tested in major well-resourced languages.
In this paper, we show that these techniques are capable of yielding improvements even in a small data scenario. We experiment with different deep neural network architectures for acoustic modeling for Northern Sámi, and report up to 50% relative error rate reductions.
We also run experiments to compare the performance of different subwords as language modeling units in Northern Sámi.


Original languageEnglish
Title of host publicationFourth International Workshop on Computational Linguistics for Uralic Languages
Publication statusPublished - 2017
MoE publication typeD3 Professional conference proceedings
EventInternational Workshop on Computational Linguistics for the Uralic Languages - Helsinki, Finland
Duration: 8 Jan 20189 Jan 2018
Conference number: 4


ConferenceInternational Workshop on Computational Linguistics for the Uralic Languages
Abbreviated titleIWCLUL

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