Use of objective speech quality measures in selecting effective spectral estimation techniques for speech enhancement

Objective quality measures are used to determine optimal enhancement performance versus variation parameter settings for the enhancement algorithms. Issues addressed include: (1) the amount of magnitude averaging needed for highest speech quality using spectral subtracting; (2) choice of spectral estimation technique for colored noise characterization; and (3) terminating criterion for best speech quality for short-time Wiener filtering and constrained iterative enhancement algorithms. Additive white Gaussian noise and slowly varying colored aircraft cockpit noise are considered in the evaluation. Results show that constrained iterative enhancement resulted in the highest quality improvement for a white Gaussian distortion in seven iterations. Over a large number of colored noise conditions the best objective qualities resulted when Bartlett and maximum-entropy spectra were used in the enhancement algorithms. For aircraft cockpit noise, further improvement was observed when noise characterization was updated more frequently