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In this paper, we propose a method combining variational autoencoder model ofspeech with a spatial clustering approach for multi-channel speech separation.The advantage of integrating spatial clustering with a spectral model was shownin several works. As the spectral model, previous works used either factorialgenerative models of the mixed speech or discriminative neural networks. In ourwork, we combine the strengths of both approaches, by building a factorialmodel based on a generative neural network, a variational autoencoder. By doingso, we can exploit the modeling power of neural networks, but at the same time,keep a structured model. Such a model can be advantageous when adapting to newnoise conditions as only the noise part of the model needs to be modified. Weshow experimentally, that our model significantly outperforms previousfactorial model based on Gaussian mixture model (DOLPHIN), performs comparablyto integration of permutation invariant training with spatial clustering, andenables us to easily adapt to new noise conditions. The code for the method isavailable at https://github.com/BUTSpeechFIT/vae_dolphin

Integration of variational autoencoder and spatial clustering for adaptive multi-channel neural speech separation