| Paper ID | SPE-6.4 |
| Paper Title |
SPEECH DEREVERBERATION USING VARIATIONAL AUTOENCODERS |
| Authors |
Deepak Baby, Amazon Alexa, Germany; Hervé Bourlard, Idiap Research Institute, Switzerland |
| Session | SPE-6: Speech Enhancement 2: Speech Separation and Dereverberation |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-ENHA] Speech Enhancement and Separation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This paper presents a statistical method for single-channel speech dereverberation using a variational autoencoder (VAE) for modelling the speech spectra. One popular approach for modelling speech spectra is to use non-negative matrix factorization (NMF) where learned clean speech spectral bases are used as a linear generative model for speech spectra. This work replaces this linear model with a powerful nonlinear deep generative model based on VAE. Further, this paper formulates a unified probabilistic generative model of reverberant speech based on Gaussian and Poisson distributions. We develop a Monte Carlo expectation-maximization algorithm for inferring the latent variables in the VAE and estimating the room impulse response for both probabilistic models. Evaluation results show the superiority of the proposed VAE-based models over the NMF-based counterparts. |
