Artificial bandwidth extension of spectral envelope with temporal clustering

We present a new wideband spectral envelope estimation framework for the artificial bandwidth extension problem. The proposed framework builds temporal clusters of the joint sub-phone patterns of the narrowband and wideband speech signals using a parallel branch HMM structure. The joint sub-phone patterns define temporally correlated neighborhoods, in which a linear prediction filter estimates spectral features of the corresponding wideband signal from the narrowband signal. The proposed framework is compared to a benchmark vector quantization based artificial bandwidth extension algorithm. Performance evaluations are performed with three distinct objective metrics and a subjective A/B test.