  Speech bandwidth expansion is crucial for expanding the frequency range of
low-bandwidth speech signals, thereby improving audio quality, clarity and
perceptibility in digital applications. Its applications span telephony,
compression, text-to-speech synthesis, and speech recognition. This paper
presents a novel approach using a high-fidelity generative adversarial network,
unlike cascaded systems, our system is trained end-to-end on paired narrowband
and wideband speech signals. Our method integrates various bandwidth upsampling
ratios into a single unified model specifically designed for speech bandwidth
expansion applications. Our approach exhibits robust performance across various
bandwidth expansion factors, including those not encountered during training,
demonstrating zero-shot capability. To the best of our knowledge, this is the
first work to showcase this capability. The experimental results demonstrate
that our method outperforms previous end-to-end approaches, as well as
interpolation and traditional techniques, showcasing its effectiveness in
practical speech enhancement applications.
