Voice activity detection based on a statistical semiparametric test

This paper adresses the voice activity detection problem within a semiparametric hypothesis testing framework. Semiparametric detection consists in combining the statistical optimality
of a parametric test with the robustness regarding the learning data of a nonparametric test. The proposed semiparametric approach splits the frame vector into two parts such that the first part has a known statistical distribution. The second part is processed by a non-parametric detector producing a binary decision. A likelihood ratio test, based on the first part and the nonparametric binary decision, is then applied to classify the frame as either speech or nonspeech. The statistical performance of the resulting fusion test is analytically established and validated using real speech signals.