HMM-based artificial bandwidth extension supported by neural networks

In telephony applications, artificial bandwidth extension (ABE) can be applied to narrowband (NB) calls for speech quality and intelligibility enhancement. However, high-band extension is challenging due to insufficient mutual information between the lower and upper frequency band in speech. Estimation errors particularly of fricatives /s, z/ are the consequence leading to annoying artifacts, such as lisping. In this paper, two neural networks are employed to support an HMM-based ABE: The first one detects /s, z/ phonemes to assist the estimation process, while the second one corrects the estimated high-band energy. In an absolute category rating test the proposed ABE attains a significantly improved speech quality vs. NB speech. This is confirmed by a comparison category rating test pointing out a speech quality gain of 1.0 CMOS points over NB speech.

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