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arXiv · 2110.12539

Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech

Marek Strong·Jonas Rohnke·Antonio Bonafonte·Mateusz Łajszczak·Trevor Wood
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Abstract

We present a Split Vector Quantized Variational Autoencoder (SVQ-VAE) architecture using a split vector quantizer for NTTS, as an enhancement to the well-known Variational Autoencoder (VAE) and Vector Quantized Variational Autoencoder (VQ-VAE) architectures. Compared to these previous architectures, our proposed model retains the benefits of using an utterance-level bottleneck, while keeping significant representation power and a discretized latent space small enough for efficient prediction from text. We train the model on recordings in the expressive task-oriented dialogues domain and show that SVQ-VAE achieves a statistically significant improvement in naturalness over the VAE and VQ-VAE models. Furthermore, we demonstrate that the SVQ-VAE latent acoustic space is predictable from text, reducing the gap between the standard constant vector synthesis and vocoded recordings by 32%.

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Marek Strong, Jonas Rohnke, Antonio Bonafonte, Mateusz Łajszczak, Trevor Wood. 2021-10-24. Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech. https://doi.org/10.21437/iberspeech.2022-1

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