A Comprehensive Study of Content Representations for Speech Synthesis
Speech content representations are central to voice conversion, speech-to-speech translation, and multimodal language models, yet they are rarely compared under a common generative framework that directly measures what each representation contains. We address this by training a generative model conditioned solely on each representation and evaluating the generated audio along the content, speaker identity, and prosody axes. Across SSL features, supervised tokens, posteriorgrams, and neural audio codecs, we find two distinct regimes: representations that nearly reconstruct the original audio, and representations that effectively disentangle speaker identity. These results show that disenta...
| Authors | Diego Torres, Axel Roebel, Nicolas Obin |
|---|---|
| Primary category | eess.AS |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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