Sequence-based Multi-lingual Low Resource Speech Recognition
Table 3: Word error rate (% WER) on the test languages when switching the SWBD data with 300 hrs equivalent of Babel.
['Model', 'Kazakh', 'Turkish', 'Haitian', 'Mongolian']
Given that adding a seemingly unrelated, but high resource language improved the performance of the model on four low resource languages, we further studied the impact of varying the source(s) of the extra data. Specifically, we replaced the 300 h Switchboard corpus with four more unrelated Babel languages, “BAB300” composed of Tamil, Amharic, Pashto, and Tagalog. We can see that adding Switchboard data outperforms adding more unrelated Babel languages.