This paper deals with the joint signal and parameter estimation for linear state-space models. An efficient solution to this prob-lem can be obtained by using a recursive instrumental variable technique based on two dual Kalman filters. In that case, the driving process and the observation noise for each filter must be white with known variances. These conditions, however, are too strong to be always satisfied in real cases. To relax them, we propose a new approach based on two dual H filters. Once a new observation of the disturbed signal is available, the first H algorithm uses the latest estimated parameters to estimate the signal, while the second H algorithm uses the estimated signal to update the parameters. In addition, as the H∞ filter behav-iour depends on the choice of various weights, we present a way to recursively tune them. This approach is then illustrated through the following cases: 1/ consistent estimation of the AR parameters from noisy observations, where the H scheme outperforms the existing algo-rithms; 2/ speech enhancement, where no a priori model of the additive noise is required for the proposed approach. In each case, a comparative study with existing methods is carried out to analyse the relevance of our solution.