A posteriori speech presence probability estimation based on averaged observations and a super-Gaussian speech model

Explicit information about speech presence or absence is needed in many speech processing applications. In a Bayesian estimation framework, this information can be provided by an a posteriori speech presence probability (SPP) estimator. Recent improvements in SPP estimation include likelihoods of speech presence based on a super-Gaussian speech model or, alternatively, based on averaged observations. In this paper, we combine these aspects and derive a closed form solution for the likelihood of speech presence based on both averaged observations and a super-Gaussian speech model. The new approach is shown to outperform competing methods that either include averaging or super-Gaussian speech models.