Speech enhancement can play a key role when aiming at friendly exchanging through the net. In that context, increasing the signal to noise ratio can be done through the use of Kalman filtering. However, Kalman filtering based-approaches usually require the explicit knowledge of the noise variances and the speech model parameters. In this paper, we propose to consider alternative approaches. Speech enhancement can be considered as an identification issue. For such a purpose, we take advantage of various results from the control framework. We present three new approaches based on subspace methods for identification. They have the advantage of minimizing the number of parameters to be estimated and avoid the use of a voice activity detector. Here, some results are proposed when speech is contaminated by additive white noise