Woosh-Mimic: Conditioning the Woosh Sound Effect Foundation Model with Vocal Mimicry

Text-to-audio generation enables semantic control over generated sounds through natural language. Adding vocal mimicry could provide an intuitive, nonverbal way to convey fine-grained temporal variations in timing, intensity, pitch, rhythm, and spectral characteristics, without describing them in words or manually specifying numerical control trajectories. However, vocal mimics and the sound effects (SFXs) they represent belong to substantially different acoustic domains, and learning the correspondence between them remains underexplored. We propose Woosh-Mimic, a vocal-mimic-conditioned SFX generation method built on the pretrained Woosh framework. Woosh-Mimic combines semantic conditioning from production SFX metadata with temporal acoustic conditioning from vocal mimics and learns from paired vocal-mimic and SFX recordings. To examine the effect of the domain mismatch, we compare a Mimic Model trained with acoustic conditions extracted from vocal mimics against a Reference Model trained with conditions extracted from the corresponding reference SFXs. The Mimic Model achieves lower Fréchet Audio Distance across all seven evaluation splits, whereas the Reference Model more strongly reflects the characteristics of the input vocal mimics in both objective and subjective evaluations. This contrast suggests that matching the conditioning domains between training and inference improves generation quality but does not necessarily improve the reflection of individual vocal mimics. Our findings highlight the potential of vocal mimicry as an expressive complement to language-based semantic control while identifying faithful control across acoustic domains as a central challenge.