ISCA Archive Interspeech 2023
ISCA Archive Interspeech 2023

Emo-StarGAN: A Semi-Supervised Any-to-Many Non-Parallel Emotion-Preserving Voice Conversion

Suhita Ghosh, Arnab Das, Yamini Sinha, Ingo Siegert, Tim Polzehl, Sebastian Stober

Speech anonymisation prevents misuse of spoken data by removing any personal identifier while preserving at least linguistic content. However, emotion preservation is crucial for natural human-computer interaction. The well-known voice conversion technique StarGANv2-VC achieves anonymisation but fails to preserve emotion. This work presents an any-to-many semi-supervised StarGANv2-VC variant trained on partially emotion-labelled non-parallel data. We propose emotion-aware losses computed on the emotion embeddings and acoustic features correlated to emotion. Additionally, we use an emotion classifier to provide direct emotion supervision. Objective and subjective evaluations show that the proposed approach significantly improves emotion preservation over the vanilla StarGANv2-VC. This considerable improvement is seen over diverse datasets, emotions, target speakers, and inter-group conversions without compromising intelligibility and anonymisation.

doi: 10.21437/Interspeech.2023-191

Cite as: Ghosh, S., Das, A., Sinha, Y., Siegert, I., Polzehl, T., Stober, S. (2023) Emo-StarGAN: A Semi-Supervised Any-to-Many Non-Parallel Emotion-Preserving Voice Conversion. Proc. INTERSPEECH 2023, 2093-2097, doi: 10.21437/Interspeech.2023-191

  author={Suhita Ghosh and Arnab Das and Yamini Sinha and Ingo Siegert and Tim Polzehl and Sebastian Stober},
  title={{Emo-StarGAN: A Semi-Supervised Any-to-Many Non-Parallel Emotion-Preserving Voice Conversion}},
  booktitle={Proc. INTERSPEECH 2023},