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Table 2: WERs (%) achieved on the monolingual and mixed segments of the FAME test set. The upper panel summarizes the number of words from each language subset. The middle panel provides the results of state-of-the-art ANN achitectures (Yılmaz et al., 2016, 2018) for reference purposes and the lower panel presents the results achieved by the ANN and SNN models in this work (AM: acoustic model).
['# of Frisian words', '# of Frisian words', 'fy 10753', 'nl 0', 'cs 1798', 'all 12551']
In this section, we apply the SNN-based ASR systems to the low-resourced ASR scenario. The overall performance (all) is also included in the rightmost column. Given that 8.5 hours Frisian and 3 hours of Dutch speech is used during the training phase, we can compare the ASR performance on different subsets, i.e. fy, nl and cs, to identify the variations in the ASR performance for different levels of low-resourcedness. We omit the results on the development set as they follow a similar pattern to the results on the test set.