Feature selection for improved bandwidth extension of speech signals

The aim of artificial bandwidth extension (BWE) is to convert speech signals with "standard telephone" quality (frequencies up to 3.4 kHz) into 7 kHz wideband speech. The principal key to high quality BWE is the estimation of the spectral envelope of the wideband speech. In general, this estimation of the wideband spectral envelope is based on a number of features that are extracted from the narrowband input speech signal. We investigate potential features and evaluate their suitability for the BWE application. The quality of each feature is quantified in terms of the statistical measures of mutual information and separability. It turns out that the best BWE results are obtained by using a large feature "super-vector" (→ high mutual information) which is subsequently reduced in dimension by a linear discriminant analysis (→ large separability). This solution also helps to reduce the computational complexity of the estimation of the wideband spectral envelope.