Vocal Intensity Conversion with Continuous SPL Control via Adversarial Training

Abstract:Vocal intensity conversion renders a recorded utterance at a different vocal intensity, rather than simply scaling its amplitude, while preserving its content and speaker identity. Existing systems control intensity through a reference utterance or a few discrete categories. Instead, we control the conversion with any target sound pressure level (SPL) on a continuous dB scale. A lightweight converter, trained as a conditional cycle-consistent GAN without parallel data, transforms the features of a frozen speech encoder, from which a pretrained vocoder resynthesizes the speech. We compare three representations, log-mel spectrograms, WavLM, and SpeechTokenizer, and introduce an objective protocol that measures the achieved intensity change against the target one on a calibrated corpus. With WavLM, the converter achieves a measured 83% of the target change in intensity and also shifts the fundamental frequency. A perceptual evaluation confirms that perceived vocal intensity increases with the target level on amplitude-normalized stimuli.

5 pages, 2 figures, 1 table. Submitted to IEEE ICASSP 2027. Code: this https URL. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)

arXiv:2610.13608 [eess.AS]

(or arXiv:2610.13608v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2610.13608 arXiv-issued DOI via DataCite (pending registration)