Résumé : Independent Factor Analysis (IFA) is used to recover latent components (or sources) from their linear observed mixtures within an unsupervised learning framework. Both the mixing process and the source densities are learned from the observed data. The sources are assumed to be mutually independent and distributed according to a mixture of Gaussians. This paper investigates the possibility of incorporating partial knowledge on the cluster belonging of some samples to estimate the IFA model. Semi-supervised and partially supervised learning cases can thus be handled. Experimental results demonstrate the ability of this approach to enhance estimation accuracy and remove indeterminacy commonly encountered in unsupervised IFA such as the permutation of the sources.