Learning Robust and Multilingual Speech Representations
Table 2: Domain transfer experiments to test the robustness of the representations to domain shifts. The models are trained on the Wall Street Journal, LibriSpeech or Tedlium and evaluated on different evaluation sets. The results on in-domain evaluation sets are in gray color. All the results are without a language model.
['[EMPTY]', '[BOLD] WSJ test92', '[BOLD] WSJ test93', '[BOLD] LibriSpeech test-clean', '[BOLD] LibriSpeech test-other', '[BOLD] Tedlium dev', '[BOLD] Tedlium test', '[BOLD] Switchboard eval2000']
CPC-LibriSpeech and CPC-8k indicate representations are learned from LibriSpeech and 8000h of speech datasets listed above respectively. The features trained on large-scale data consistently outperform other representations across different evaluation sets. The speech recognition models trained on the Wall Street Journal perform badly on phone call data in general. However, CPC representations learned on large datasets are more robust than those trained only on read English data (LibriSpeech).