Benchmarking Generative Latent Variable Models for SpeechDownload PDF

Published: 29 Mar 2022, Last Modified: 22 Oct 2023ICLR 2022 DGM4HSD workshop PosterReaders: Everyone
Keywords: generative models, latent variable models, variational autoencoder, generative speech modelling, benchmark, likelihood, phoneme recognition
TL;DR: We benchmark a set of generative models based on the VAE on speech modeling and phoneme recognition using a well-motivated output distribution.
Abstract: Stochastic latent variable models (LVMs) achieve state-of-the-art performance on natural image generation but are still inferior to deterministic models on speech. In this paper, we develop a speech benchmark of popular temporal LVMs and compare them against state-of-the-art deterministic models. We report the likelihood, which is a much used metric in the image domain, but rarely, or incomparably, reported for speech models. To assess the quality of the learned representations, we also compare their usefulness for phoneme recognition. Finally, we adapt the Clockwork VAE, a state-of-the-art temporal LVM for video generation, to the speech domain. Despite being autoregressive only in latent space, we find that the Clockwork VAE can outperform previous LVMs and reduce the gap to deterministic models by using a hierarchy of latent variables.
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 2 code implementations](
3 Replies