Kernel Method for Voice Activity Detection in the Presence of Transients

Voice activity detection in the presence of transient interferences is a challenging problem since transients are often detected incorrectly as speech by existing detectors. In this paper, we deviate from traditional approaches and take a geometric standpoint, in which the key element in obtaining an accurate voice activity detection is finding a metric that appropriately distinguishes between speech and transients. For example, speech and transients may often appear similar through the Euclidean distance when represented, e.g., by the Mel-frequency cepstral coefficients, thereby resulting in incorrect speech detection. To address this challenge, we propose to use a metric based on the statistics of the signal in short temporal windows and justify its use by modeling speech and transients by their latent generating variables. These latent variables may be related to physical constraints controlling the generation of the signal, and, as such, they accurately represent the content of the signal - speech or transient. We show that the Euclidean distance between the latent variables is approximated by the proposed metric. Then, by incorporating this metric into a kernel-based manifold learning method, we devise a measure of voice activity and show it leads to improved detection scores compared with competing detectors.