Towards Language-Universal End-to-End Speech Recognition
Table 2: Performance of the language-universal modeling approach with and without the language-specific gating. Baseline monolingual performance is also shown. Each language has 150 h of training data.
['Training Data', 'Lang Gate', '% CER EN', '% CER DE', '% CER ES', '% WER EN', '% WER DE', '% WER ES']
We next evaluated the proposed language-universal model with language-specific gating. Our model showed 10.7%, 11.4%, and 14.1% relative improvement in CER compared to a monolingual for EN, DE, and ES respectively. The relative improvements in WER, which was calculated without using any language model, were 7.0%, 8.6%, and 11.1%, for EN, DE, and ES respectively. Furthermore, when language-specific gating is used, additional improvement is obtained when the number of training languages increased from two to three.