  In previous work, we proposed a variational autoencoder-based (VAE) Bayesian
permutation training speech enhancement (SE) method (PVAE) which indicated that
the SE performance of the traditional deep neural network-based (DNN) method
could be improved by deep representation learning (DRL). Based on our previous
work, we in this paper propose to use $\beta$-VAE to further improve PVAE's
ability of representation learning. More specifically, our $\beta$-VAE can
improve PVAE's capacity of disentangling different latent variables from the
observed signal without the trade-off problem between disentanglement and
signal reconstruction. This trade-off problem widely exists in previous
$\beta$-VAE algorithms. Unlike the previous $\beta$-VAE algorithms, the
proposed $\beta$-VAE strategy can also be used to optimize the DNN's structure.
This means that the proposed method can not only improve PVAE's SE performance
but also reduce the number of PVAE training parameters. The experimental
results show that the proposed method can acquire better speech and noise
latent representation than PVAE. Meanwhile, it also obtains a higher
scale-invariant signal-to-distortion ratio, speech quality, and speech
intelligibility.
