  Recent advancements in AI have democratized its deployment as a healthcare
assistant. While pretrained models from large-scale visual and audio datasets
have demonstrably generalized to this task, surprisingly, no studies have
explored pretrained speech models, which, as human-originated sounds,
intuitively would share closer resemblance to lung sounds. This paper explores
the efficacy of pretrained speech models for respiratory sound classification.
We find that there is a characterization gap between speech and lung sound
samples, and to bridge this gap, data augmentation is essential. However, the
most widely used augmentation technique for audio and speech, SpecAugment,
requires 2-dimensional spectrogram format and cannot be applied to models
pretrained on speech waveforms. To address this, we propose RepAugment, an
input-agnostic representation-level augmentation technique that outperforms
SpecAugment, but is also suitable for respiratory sound classification with
waveform pretrained models. Experimental results show that our approach
outperforms the SpecAugment, demonstrating a substantial improvement in the
accuracy of minority disease classes, reaching up to 7.14%.
