New Baseline in Automatic Speech Recognition for Northern Sámi

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.

DOI

10.18653/v1/W18-0208

Citation

Leinonen , J , Smit , P , Virpioja , S & Kurimo , M 2017 , New Baseline in Automatic Speech Recognition for Northern Sámi . in Fourth International Workshop on Computational Linguistics for Uralic Languages . Association for Computational Linguistics , pp. 89-99 , International Workshop on Computational Linguistics for the Uralic Languages , Helsinki , Finland , 08/01/2018 . https://doi.org/10.18653/v1/W18-0208

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201802091229

Collections

[article-cris] Sähkötekniikan korkeakoulu / ELEC

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