On using acoustic environment classification for statistical model-based speech enhancement

Abstract

In this paper, we present a statistical model-based speech enhancement technique using acoustic environment classification supported by a Gaussian mixture model (GMM). In the data training stage, the principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method, the long-term smoothing parameter of the noise estimation, and the control parameter of the minimum gain value are uniquely set as optimal operating points according to the given noise information to ensure the best performance for each noise. These optimal operating points, which are specific to the different background noises, are estimated based on the composite measures, which are the objective quality measures representing the highest correlation with the actual speech quality processed by noise suppression algorithms. In the on-line environment-aware speech enhancement step, the noise classification is performed on a frame-by-frame basis using the maximum likelihood (ML)-based Gaussian mixture model (GMM). The speech absence probability (SAP) is used to detect the speech absence periods and to update the likelihood of the GMM. According to the classified noise information for each frame, we assign the optimal values to the aforementioned three parameters for speech enhancement. We evaluated the performances of the proposed methods using objective speech quality measures and subjective listening tests under various noise environments. Our experimental results showed that the proposed method yields better performances than does a conventional algorithm with fixed parameters.