  Recent advances in foundation models have enabled audio-generative models
that produce high-fidelity sounds associated with music, events, and human
actions. Despite the success achieved in modern audio-generative models, the
conventional approach to assessing the quality of the audio generation relies
heavily on distance metrics like Frechet Audio Distance. In contrast, we aim to
evaluate the quality of audio generation by examining the effectiveness of
using them as training data. Specifically, we conduct studies to explore the
use of synthetic audio for audio recognition. Moreover, we investigate whether
synthetic audio can serve as a resource for data augmentation in speech-related
modeling. Our comprehensive experiments demonstrate the potential of using
synthetic audio for audio recognition and speech-related modeling. Our code is
available at https://github.com/usc-sail/SynthAudio.
