SDP-Codec: A Speaker-Decoupled Speech Codec with Pitch Injection for Low-Bitrate Coding and Zero-Shot Voice Conversion
Abstract
Speaker-decoupled speech codecs can reduce bitrate by separating global speaker attributes from local content and prosody, while supporting voice conversion. Existing speaker-decoupled codecs face a trade-off: methods that explicitly suppress speaker leakage often rely on multi-stage or auxiliary training, whereas simpler designs can leave residual speaker information in local tokens. We propose SDP-Codec, a speaker-decoupled, pitch-injected codec trained with a single-stage optimization pipeline. SDP-Codec derives local tokens from continuous pre-quantization features of a pretrained self-supervised encoder and injects normalized F0 via a pitch encoder-decoder with global-conditioned denormalization and soft-label pitch reconstruction objective. Across 16 kHz and 24 kHz settings, SDP-Codec achieves competitive reconstruction and strong zero-shot voice conversion at comparable bitrates, with the lowest speaker-probing accuracy among compared systems, suggesting reduced speaker leakage.
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Research summary
SDP-Codec presents a compelling single-stage approach to speaker-decoupled speech coding, effectively balancing reconstruction quality and voice conversion capability through innovative pitch injection and soft-label loss mechanisms. The rigorous evaluation, including speaker probing and comprehensive ablation, strengthens the claim that the method successfully reduces speaker leakage in local tokens while preserving content and prosody, offering a practical solution for low-bitrate neural speech codecs.
Methodology
The paper proposes SDP-Codec, a speaker-decoupled speech codec designed to minimize bitrate while supporting zero-shot voice conversion. The core methodological contribution lies in the decoupling strategy: it uses a single-stage optimization pipeline where local tokens are derived from continuous pre-quantization features of a pretrained vq-wav2vec encoder, rather than the quantized units themselves. To address the loss of prosodic information inherent in this compression, the authors inject normalized F0 via a dedicated pitch encoder-decoder. A key innovation is the use of a soft-label pitch reconstruction objective (Gaussian-blurred one-hot bins) and global-conditioned denormalization, which allows the global branch to handle speaker-dependent pitch range while the local branch handles content and normalized prosody. The global branch utilizes WavLM features compressed via a perceiver resampler to provide time-invariant speaker embeddings. This design attempts to resolve the trade-off between explicit speaker suppression (complex training) and residual leakage (poor VC).
Results & evaluation
The evaluation covers reconstruction quality (UTMOS, WER, STOI) and zero-shot voice conversion (SECS, F0 correlation, NMOS, SMOS) at 16 kHz and 24 kHz. The paper compares SDP-Codec against strong baselines including LSCodec, BiCodec, MSRCodec, and EZ-VC. Results indicate that SDP-Codec achieves competitive reconstruction quality, particularly in STOI and F0 correlation, while demonstrating superior speaker similarity in VC tasks compared to other codec-based systems. The speaker-probing accuracy experiment is a strong addition, quantitatively demonstrating reduced speaker leakage in the local tokens compared to competitors. The ablation studies effectively validate the components, showing that the soft-label pitch loss and the use of continuous pre-quantization features are critical for performance.
Reproducibility
The paper provides source code and a demo link. Training details are provided, including dataset splits (LibriSpeech, LibriTTS, MLS), training steps, and hardware. However, the use of "internal" data for some baselines (Vevo) and the specific configuration of baselines (e.g., merging thresholds) to match bitrates introduces some variability. The description of the architecture is sufficiently detailed for replication, and the frozen pretrained components (vq-wav2vec, WavLM, FCPE) are standard.
Limitations
The paper acknowledges that content fidelity (WER) remains a limitation, trading some reconstruction quality for VC performance. The reliance on a pretrained pitch extractor (FCPE) introduces a dependency on external tools for feature extraction, which might not be perfectly aligned with the codec's internal representations. The evaluation is primarily on English datasets (LibriSpeech, LibriTTS, MLS), limiting the assessment of multilingual robustness, although the authors mention Indic languages in the table header, the main text focuses on English. The single-stage training, while simpler, might not achieve the same level of disentanglement as multi-stage methods that explicitly optimize for speaker suppression.
Broader impact
SDP-Codec contributes to the field of efficient speech representation learning, enabling low-bitrate transmission and flexible voice conversion. This has implications for telecommunication, speech language models (SLMs) by providing compact tokens, and creative applications in voice cloning and conversion. The reduced speaker leakage is a positive step towards more ethical and controllable voice conversion systems. However, the potential for misuse in deepfake generation remains a concern, though the low bitrate and specific use case mitigate this slightly compared to high-fidelity generative models. SDP-Codec presents a compelling single-stage approach to speaker-decoupled speech coding, effectively balancing reconstruction quality and voice conversion capability through innovative pitch injection and soft-label loss mechanisms. The rigorous evaluation, including speaker probing and comprehensive ablation, strengthens the claim that the method successfully reduces speaker leakage in local tokens while preserving content and prosody, offering a practical solution for low-bitrate neural speech codecs.
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