On the use of Bayesian modeling for predicting noise reduction performance

In speech enhancement applications, a validated metric of noise reduction performance is vital in the relative ranking of noise reduction algorithms and in enhancing the performance of a noise reduction algorithm. subjective scores of enhanced speech remain the yardstick for performance, but objective metrics that emulate subjective evaluations are preferred for cost- and time-effectiveness. In this paper, we analyze the performance of two objective methods for predicting the quality of enhanced speech. The first method employs the coherence-based speech intelligibility index, while the second method uses features derived from the Moore-Glasberg auditory model. In both cases, the features are mapped to a quality score using the Bayesian modeling approach. Results show that the combination of the auditory model-based feature set and the Bayesian modeling provides the best performance in predicting the quality scores of enhanced speech.