WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling
Integrating speech understanding and generation is a pivotal step toward building unified speech models. However, the different representations required for these two tasks currently pose significant compatibility challenges. Typically, semantics-oriented features are learned from self-supervised learning (SSL), and acoustic-oriented features from reconstruction. Such fragmented representations hinder the realization of truly unified speech systems. We present WavCube, a compact continuous latent derived from an SSL speech encoder that simultaneously supports speech understanding, reconstruction, and generation. WavCube employs a two-stage training scheme. Stage 1 trains a semantic bottle...
| Authors | Guanrou Yang, Tian Tan, Qian Chen et al. |
|---|---|
| Primary category | eess.AS |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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