Abstract. In this paper, we are interested in unsupervised (unknown noise) speech enhancement, where the probability distribution of clean speech spectrogram is simulated via a latent variable generative model, also called the decoder. Recently, variational autoencoders (VAEs) have gained much popularity as probabilistic generative models. In VAEs, the posterior of the latent variables is computationally intractable, and it is approximated by a so-called encoder network. Motivated by the fact that visual data, i.e. lip images of the speaker, provide helpful and complementary information about speech, some audio-visual architectures have been recently proposed. The initialization of the latent variables at test time is crucial as the overall inference problem is non-convex. This is usually done by using the output of the encoder where the noisy audio and clean video data are given as input. Current audio-visual models do not provide an effective initialization because the two modalities are tightly coupled (concatenated) in the associated architectures. To overcome this issue, we inspire from mixture models, and introduce the mixture of inference networks variational autoencoder (MIN-VAE). Two encoder networks input, respectively, audio and visual data, and the posterior of the latent variables is modeled as a mixture of two Gaussian distributions output from each encoder network. The mixture variable is also latent, and therefore the inference of learning the optimal balance between the audio and visual inference network is unsupervised as well. By training a shared decoder, the overall network learns to adaptively fuse the two modalities. Moreover, at test time, the video encoder, which takes (clean) visual data, is used for initialization. A variational inference approach is derived to train the proposed generative model. Thanks to the novel inference procedure and the robust initialization, the proposed audio-visual VAE exhibits superior performance on speech enhancement than using the standard audio-only as well as audio-visual counterparts.