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Towards Generative Latent Variable Models for Speech

While stochastic latent variable models (LVMs) now achieve state-of-the-art performance on natural image generation, they are still inferior to deterministic models on speech. On natural images, these models have been parameterised with very deep hierarchies of latent variables, but research shows that these model constructs are not directly applicable to sequence data. In this paper, we benchmark popular temporal LVMs against state-of-the-art deterministic models on speech. We report the likelihood, which is a much used metric in the image domain but rarely, and often incomparably, reported for speech models. This is prerequisite work needed for the research community to improve LVMs on speech. We adapt Clockwork VAE, a state-of-the-art temporal LVM for video generation, to the speech domain, similar to how WaveNet adapted PixelCNN from images to speech. Despite being autoregressive only in latent space, we find that the Clockwork VAE outperforms previous LVMs and reduces the gap to deterministic models by using a hierarchy of latent variables.

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