A scalable codec for bone-conducted speech based on generative and diffusion models

In extremely noisy communication scenarios, the bone-conducted microphone (BCM) speech codec is often combined with speech bandwidth extension to improve the BCM speech quality. However, this tandem approach leads to a complex system architecture. To address the problem, a scalable codec for BCM speech based on generative and diffusion probabilistic models is proposed in this paper. Specifically, a specialized codec architecture is constructed to encode BCM speech while complementing its high-frequency components. Then, a key feature extraction block is presented to address the diminishing memory capacity in shallow layers as the network depth increases. Next, considering the potential lack of high-frequency detail information, an overall refinement block is introduced to refine the reconstructed speech signals. Finally, based on the U-Net architecture, a diffusion probability model is proposed to upsample the input audio signal from a bandwidth of 8 kHz to a high-resolution audio signal with a bandwidth of 20 kHz and a sampling rate of 48 kHz. The proposed method can simultaneously encode and improve BCM speech quality using a single network. It supports different bitrate settings without architectural changes or retraining and dynamically adjusts transmitted data based on changing network load. Simulation experiments demonstrate its feasibility.