Robust bandwidth extension of noise-corrupted narrowband speech

We present a new bandwidth extension algorithm for converting narrowband telephone speech into wideband speech using a transformation in the mel cepstral domain. Unlike previous approaches, the proposed method is designed specifically for bandwidth extension of narrowband speech that has been corrupted by environmental noise. We show that by exploiting previous research in mel cepstrum feature enhancement, we can create a unified probabilistic framework under which the feature denoising and bandwidth extension processes are tightly integrated using a single shared statistical model. By doing so, we are able to both denoise the observed narrowband speech and robustly extend its bandwidth in a jointly optimal manner. A series of experiments on clean and noise-corrupted narrowband speech is performed to validate our approach.