CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space
Speech Bandwidth Extension improves clarity and intelligibility by restoring/inferring appropriate high-frequency content for low-bandwidth speech. Existing methods often rely on spectrogram or waveform modeling, which can incur higher computational cost and have limited high-frequency fidelity. Neural audio codecs offer compact latent representations that better preserve acoustic detail, yet accurately recovering high-resolution latent information remains challenging due to representation mismatch. We present CodecFlow, a neural codec-based BWE framework that performs efficient speech reconstruction in a compact latent space. CodecFlow employs a voicing-aware conditional flow converter o...
| Authors | Bowen Zhang, Junchuan Zhao, Ian McLoughlin et al. |
|---|---|
| Primary category | cs.SD |
| Published | 2 March 2026 |
| Last updated at source | 11 October 2026 |
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