Building an ASR Corpus Using Althingi’s Parliamentary Speeches

Inga Rún Helgadóttir, Róbert Kjaran, Anna Björk Nikulásdóttir, Jón Guðnason


Acoustic data acquisition for under-resourced languages is an important and challenging task. In the Icelandic parliament, Althingi, all performed speeches are transcribed manually and published as text on Althingi’s web page. To reduce the manual work involved, an automatic speech recognition system is being developed for Althingi. In this paper the development of a speech corpus suitable for the training of a parliamentary ASR system is described. Text and audio data of manually transcribed speeches were processed to build an aligned, segmented corpus, whereby language specific tasks had to be developed specially for Icelandic. The resulting corpus of 542 hours of speech is freely available on http://www.malfong.is. First experiments with an ASR system trained on the Althingi corpus have been conducted, showing promising results. Word error rate of 16.38% was obtained using time-delay deep neural network (TD-DNN) and 14.76% was obtained using long-short term memory recurrent neural network (LSTM-RNN) architecture. The Althingi corpus is to our knowledge the largest speech corpus currently available in Icelandic. The corpus as well as the developed methods for corpus creation constitute a valuable resource for further developments within Icelandic language technology.


 DOI: 10.21437/Interspeech.2017-903

Cite as: Helgadóttir, I.R., Kjaran, R., Nikulásdóttir, A.B., Guðnason, J. (2017) Building an ASR Corpus Using Althingi’s Parliamentary Speeches. Proc. Interspeech 2017, 2163-2167, DOI: 10.21437/Interspeech.2017-903.


@inproceedings{Helgadóttir2017,
  author={Inga Rún Helgadóttir and Róbert Kjaran and Anna Björk Nikulásdóttir and Jón Guðnason},
  title={Building an ASR Corpus Using Althingi’s Parliamentary Speeches},
  year=2017,
  booktitle={Proc. Interspeech 2017},
  pages={2163--2167},
  doi={10.21437/Interspeech.2017-903},
  url={http://dx.doi.org/10.21437/Interspeech.2017-903}
}