A Benchmark of Dynamical Variational Autoencoders applied to Speech Spectrogram Modeling
Abstract
The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, input data vectors are processed independently. In recent years, a series of papers have presented different extensions of the VAE to process sequential data, that not only model the latent space, but also model the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks. We recently performed a comprehensive review of those models and unified them into a general class called Dynamical Variational Autoencoders (DVAEs). In the present paper, we present the results of an experimental benchmark comparing six of those DVAE models on the speech analysis-resynthesis task, as an illustration of the high potential of DVAEs for speech modeling.
Cite
@article{arxiv.2106.06500,
title = {A Benchmark of Dynamical Variational Autoencoders applied to Speech Spectrogram Modeling},
author = {Xiaoyu Bie and Laurent Girin and Simon Leglaive and Thomas Hueber and Xavier Alameda-Pineda},
journal= {arXiv preprint arXiv:2106.06500},
year = {2021}
}
Comments
Accepted to Interspeech 2021. arXiv admin note: text overlap with arXiv:2008.12595