Text-to-speech synthesis based on latent variable conversion using diffusion probabilistic model and variational autoencoder
This is an incremental improvement in TTS for applications requiring handling of noisy or misaligned text-speech data.
The paper tackles text-to-speech synthesis by proposing a method that uses a diffusion probabilistic model and variational autoencoder for latent variable conversion, showing robustness to poor linguistic labels and alignment errors in experiments.
Text-to-speech synthesis (TTS) is a task to convert texts into speech. Two of the factors that have been driving TTS are the advancements of probabilistic models and latent representation learning. We propose a TTS method based on latent variable conversion using a diffusion probabilistic model and the variational autoencoder (VAE). In our TTS method, we use a waveform model based on VAE, a diffusion model that predicts the distribution of latent variables in the waveform model from texts, and an alignment model that learns alignments between the text and speech latent sequences. Our method integrates diffusion with VAE by modeling both mean and variance parameters with diffusion, where the target distribution is determined by approximation from VAE. This latent variable conversion framework potentially enables us to flexibly incorporate various latent feature extractors. Our experiments show that our method is robust to linguistic labels with poor orthography and alignment errors.