Generalized likelihood ratio test for voiced/unvoiced decision using the harmonic plus noise model

In this paper, a novel method for voiced/invoiced decision in speech and music signals is presented. Voiced/unvoiced decision is required for many applications, including better modeling for analysis/synthesis, detection of model changes for segmentation purposes and better signal characterization for indexing and recognition applications. The proposed method is based on the generalized likelihood ratio test (GLRT) and assumes colored Gaussian noise with unknown covariance. Under voiced hypothesis, a harmonic plus noise model is assumed. The derived method is combined with a maximum a-posteriori probability (MAP) scheme to obtain a voiced unvoiced tracking algorithm. The performance of the proposed method is tested under the Keele University database for different signal-to-noise ratios (SNRs), and the results show that the algorithm performs well even under severe noise conditions.