HMM-based artificial bandwidth extension supported by neural networks

Abstract

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.