Voice activity detection in presence of transient noise using spectral clustering and diffusion kernels

In this paper, we introduce a voice activity detection (VAD) algorithm based on spectral clustering and diffusion kernels. The proposed algorithm is a supervised learning algorithm comprising of learning and testing stages: A sample cloud is produced for every signal frame by utilizing a moving window. Mel-frequency cepstrum coefficients (MFCCs) are then calculated for every sample in the cloud in order to produce an MFCC matrix and subsequently a covariance matrix for every frame. Utilizing the covariance matrix, we calculate a similarity matrix using spectral clustering and diffusion kernels methods. Using the similarity matrix, we cluster the data and transform it to a new space where each point is labeled as speech or nonspeech. We then use a Gaussian Mixture Model (GMM) in order to build a statistical model for labeling data as speech or nonspeech. Simulation results demonstrate its advantages compared to a recent VAD algorithm.