Data Augmenting Contrastive Learning of Speech Representationsin the Time Domain
Table 3: ABX errors on the ZeroResource Speech Challenge 2017 (120s). Within- (“W.”) and across-speaker (“A.”) phoneme discriminability scores on English, French and Mandarin speech for CPC features with and without data augmentation. For comparison, the best systems plus supervised topline of the ZeroSpeech leaderboard trained on the provided datasets.
['[EMPTY]', 'English W.', 'English A.', 'French W.', 'French A.', 'Mandarin W.', 'Mandarin A.', 'AVG']
As can be seen, while noise augmentation improves the score on all three languages, we cannot reach the SOTA with the small training datasets provided from the challenge. We can however, be on par with or improve over best performing baseline with our out-of-domain train sets (same languages, larger datasets), in particular with the larger model. This shows that while our technique scales with dataset size, it is still less data efficient than the techniques described in Heck et al. [heck2017] and Choroskwi et al. [chor2019]. Note however, that both studies used speaker adaptation which are outside the scope of what can be done with standard time domain data augmentation techniques.