aiqbench analysis
This research demonstrates a novel method for disentangling linguistic content from paralinguistic features like speaker identity and emotion within frozen speech encoders using routed sparse autoencoders. The study confirms that this factor-specific separation persists across different corpora and can be manipulated via feature-space interventions without retraining the base models. Such advances in representation learning are critical for improving robustness in downstream AI tasks that require strict isolation of specific audio attributes.
Disentangling Linguistic and Paralinguistic Information with Routed Sparse Autoencoders
arXiv:2610.10865v1 Announce Type: new Abstract: Self-supervised speech encoders contain linguistic and paralinguistic information in a shared, entangled representation space. We combine a TopK sparse autoencoder with route-specific supervision and cross-factor adversaries. Across frozen SPEAR and WavLM encoders, independent probes show factor-specific retention and suppression: linguistic information remains stronger in the linguistic route, while paralinguistic factors, including speaker identity, emotion, and prosody, are retained in the paralinguistic route and substantially reduced in the linguistic route. The route organisation learned on LibriSpeech persists on MSP-Podcast without representation-side retraining. Feature-space route interventions further transfer the swapped factor while largely preserving the information carried by the unchanged route. These results show consistent route-selective separation across encoders, corpora, independent probes, and representation-level interventions.
Abstract:Self-supervised speech encoders contain linguistic and paralinguistic information in a shared, entangled representation space. We combine a TopK sparse autoencoder with route-specific supervision and cross-factor adversaries. Across frozen SPEAR and WavLM encoders, independent probes show factor-specific retention and suppression: linguistic information remains stronger in the linguistic route, while paralinguistic factors, including speaker identity, emotion, and prosody, are retained in the paralinguistic route and substantially reduced in the linguistic route. The route organisation learned on LibriSpeech persists on MSP-Podcast without representation-side retraining. Feature-space route interventions further transfer the swapped factor while largely preserving the information carried by the unchanged route. These results show consistent route-selective separation across encoders, corpora, independent probes, and representation-level interventions.
Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
arXiv:2610.10865 [cs.CL]
(or arXiv:2610.10865v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.10865 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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