Disentangling Speech Representations Learning With Latent Diffusion for Speaker Verification

Disentangled speech representation learning for speaker verification aims to separate spoken content and speaker timbre into distinct representations. However, existing variational autoencoder (VAE)–based methods for speech disentanglement rely on latent variables that lack semantic meaning, limiting their effectiveness for speaker verification. To address this limitation, we propose a diffusion-based method that disentangles and separates speaker features and speech content in the latent space. Building upon the VAE framework, we employ a speaker encoder to learn latent variables representing speaker features while using frame-specific latent variables to capture content. Unlike previous sequential VAE approaches, our method utilizes a conditional diffusion model in the latent space to derive speaker-aware representations. Experiments on the VoxCeleb and CN-Celeb datasets demonstrate that our method effectively isolates speaker features from speech content using pre-trained speech representations. The learned embeddings are robust to language mismatches since the speaker embeddings become content-invariant after content removal. Additionally, we design contrastive learning experiments showing that our training objective can enhance the learning of speaker-discriminative embeddings without relying on classification-based loss.