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Open ASR for Icelandic: Resources and a Baseline System

Developing language resources is an important task when creating a speech recognition system for a less-resourced language. In this paper we describe available language resources and their preparation for use in a large vocabulary speech recognition (LVSR) system for Icelandic. The content of a speech corpus is analysed and training and test sets compiled, a pronunciation dictionary is extended, and text normalization for language modeling performed. An ASR system based on neural networks is implemented using these resources and tested using different acoustic training sets. Experimental results show a clear increase in word-error-rate (WER) when using smaller training sets, indicating that extension of the speech corpus for training would improve the system. When testing on data with known vocabulary only, the WER is 7.99%, but on an open vocabulary test set the WER is 15.72%. Furthermore, impact of the content of the acoustic training corpus is examined. The current results indicate that an ASR system could profit from carefully selected phonotactical data, however, further experiments are needed to verify this impression. The language resources are available on http://malfong.is and the source code of the project can be found on https://github.com/cadia-lvl/ice-asr/tree/master/ice-kaldi.