voice activity detection using clustering-based method in spectro-temporal features space

This paper proposes a novel method for voice activity detection based on clustering in the spectro-temporal domain. in the proposed algorithms, the auditory model is used in order to extract the spectro-temporal features. the gaussian mixture model and the wk-means clustering methods are used to decrease the dimensions of the spectro-temporal space. moreover, the energy and positions of the clusters are used for voice activity detection. silence/speech is recognized using the attributes of clusters and the updated threshold value in each frame. having a higher energy, the first cluster is used as the main speech section in computation. the efficiency of the proposed method is evaluated for silence/speech discrimination in different noisy conditions. displacement of the clusters in the spectro-temporal domain is considered as the criterion to determine the robustness of the features. according to the results obtained, the proposed method improves the speech/non-speech segmentation rate in comparison to the temporal and spectral features in low signal to noise ratios (snrs).