Speech spectral amplitude estimators using optimally shaped Gamma and Chi priors

In this paper, four STFT based speech enhancement algorithms are proposed. The algorithms enhance speech by estimating its short time spectral amplitude and are combinations of two estimators (MMSE and MAP) with two speech spectral amplitude priors (Gamma and Chi). The proposed priors have a shape parameter a, whose effect on the quality of speech is a focal point of our investigation. Rather than using a priori estimated values of a, we seek those values that maximise the quality of the enhanced speech, in an a posteriori fashion. The performance of the algorithms is first evaluated as a function of the shape parameter a and optimal values are then sought by means of a formal subjective listening test. Finally, the parallel examination of four speech enhancement algorithms offers an insight into the relative importance of the employed priors and estimators, as the proposed algorithms are only different with respect to these two elements.