Multi-Graph Decoding for Code-Switching ASR
Table 3: WER (%) obtained on the development and test set of the FAME! Corpus
['[EMPTY]', '[EMPTY]', '[EMPTY]', 'Dev. fy', 'Dev. nl', 'Dev. fy-nl', 'Dev. all', 'Test fy', 'Test nl', 'Test fy-nl', 'Test all', 'Total']
The upper panel summarizes the number of Frisian and Dutch words in each component of the development and test sets. The middle panel shows the baseline results provided by the single-graph CS system using the baseline CS LM and its interpolated versions with the larger Dutch LMs (cs-nl+ and cs-nl++). The baseline CS ASR system provides a WER of 24.6% (20.9%) on the Dutch subset of the development (test) set respectively. Interpolating the largest Dutch LMs brings marginal improvements reducing the WER to 24.1% (20.1%). The best results after LM rescoring are obtained using the RNN-LM trained on the bilingual text (107M words) for all single-graph systems. All three systems provide similar overall performance with a total WER of 22.5%, while the best performance on the Dutch subset is provided by the interp-nl++ system with a WER of 23.1% (18.8%).