Improved speech absence probability estimation based on environmental noise classification

An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement. A relevant noise estimation approach, known as the speech presence uncertainty tracking method, requires seeking the “a priori” probability of speech absence that is derived by applying microphone input signal and the noise signal based on the estimated value of the “a posteriori” signal-to-noise ratio (SNR). To overcome this problem, first, the optimal values in terms of the perceived speech quality of a variety of noise types are derived. Second, the estimated optimal values are assigned according to the determined noise type which is classified by a real-time noise classification algorithm based on the Gaussian mixture model (GMM). The proposed algorithm estimates the speech absence probability using a noise classification algorithm which is based on GMM to apply the optimal parameter of each noise type, unlike the conventional approach which uses a fixed threshold and smoothing parameter. The performance of the proposed method was evaluated by objective tests, such as the perceptual evaluation of speech quality (PESQ) and composite measure. Performance was then evaluated by a subjective test, namely, mean opinion scores (MOS) under various noise environments. The proposed method show better results than existing methods.