Data Augmenting Contrastive Learning of Speech Representationsin the Time Domain
Table 1: ABX errors on data-augmented CPC features (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of types of data augmentation, in isolation or combination (see Section 4.1).
['[EMPTY]', 'Within spk. dev', 'Within spk. dev', 'Across spk. dev', 'Across spk. dev']
The only augmentation performing better on past+future is add. According to their average performance, the individual augmentations can be sorted, from most to least useful: pitch, add, reverb, tdrop, and bandrej. Next, we study the performance of combinations of augmentations. We decided to drop bandrej from consideration due to its poor results. We only consider augmenting past, as this gives roughly the same quality of representations, but requires less computation. As a result, we have 6 possible two-way, 4 three-way, and 1 four-way combination of effects.