aiqbench analysis
This study provides a rigorous comparative analysis of speech content representations, revealing how information capacity and training objectives interact to disentangle speaker identity from audio content. By evaluating SSL features, supervised tokens, and neural codecs under a unified generative framework, the research clarifies the fundamental trade-offs between audio reconstruction fidelity and feature separation. These findings offer critical insights for advancing multimodal language models and voice conversion systems that rely on robust speech representations.
A Comprehensive Study of Content Representations for Speech Synthesis
arXiv:2609.30975v1 Announce Type: cross Abstract: 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 disentanglement depends not on supervision alone, but on the interaction between the training objective and the representation's information capacity: supervised representations only disentangle speaker identity when their capacity is sufficiently constrained.
Abstract: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 disentanglement depends not on supervision alone, but on the interaction between the training objective and the representation's information capacity: supervised representations only disentangle speaker identity when their capacity is sufficiently constrained.
Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
arXiv:2609.30975 [eess.AS]
(or arXiv:2609.30975v1 [eess.AS] for this version)
https://doi.org/10.48550/arXiv.2609.30975 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 222 words
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