Multi-Graph Decoding for Code-Switching ASR
Table 2: WER (%) obtained on the monolingual utterances in the development and test set of the FAME! Corpus
['[EMPTY]', '[EMPTY]', 'Dev. fy', 'Dev. nl', 'Test fy', 'Test nl']
The ASR system using only the Frisian (fy) graph provides a WER of 22.8% (21.9%) on the Frisian segments. These results are similar to the recognition accuracies provided by the baseline systems indicating that the ASR performance of the baseline CS system on monolingual Frisian segments is on a par with the results that a monolingual Frisian ASR can achieve. On the other hand, only using the largest monolingual Dutch graph yields a WER of 17.8% (15.8%) on the Dutch segments which reveals the largest performance gain that could be obtained on the Dutch segments using a larger Dutch LM. Finally, the tri-graph systems (union-fy-nl, union-fy-nl+, and union-fy-nl++) with different Dutch graphs of varying sizes provide comparable results with the corresponding bi-graph systems which do not use a monolingual Frisian graph. The LM rescoring using three RNN-LMs (trained on the fy (37M), cs (107M) and nl++ (309M) text corpora) results in a reduced recognition accuracy on the fy segments due to the inferior performance of the fy RNN-LM compared to the cs RNN-LM.