Towards Robust FastSpeech 2 by Modelling Residual Multimodality

• URL: http://arxiv.org/abs/2306.01442v1
• Date: Fri, 2 Jun 2023 11:03:26 GMT
• Title: Towards Robust FastSpeech 2 by Modelling Residual Multimodality
• Authors: Fabian K\"ogel, Bac Nguyen, Fabien Cardinaux
• Abstract summary: State-of-the-art non-autoregressive text-to-speech models based on FastSpeech 2 can efficiently synthesise high-fidelity and natural speech. We observe characteristic audio distortions in expressive speech datasets. TVC-GMM reduces spectrogram smoothness and improves perceptual audio quality in particular for expressive datasets.
• Score: 4.4904382374090765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art non-autoregressive text-to-speech (TTS) models based on FastSpeech 2 can efficiently synthesise high-fidelity and natural speech. For expressive speech datasets however, we observe characteristic audio distortions. We demonstrate that such artefacts are introduced to the vocoder reconstruction by over-smooth mel-spectrogram predictions, which are induced by the choice of mean-squared-error (MSE) loss for training the mel-spectrogram decoder. With MSE loss FastSpeech 2 is limited to learn conditional averages of the training distribution, which might not lie close to a natural sample if the distribution still appears multimodal after all conditioning signals. To alleviate this problem, we introduce TVC-GMM, a mixture model of Trivariate-Chain Gaussian distributions, to model the residual multimodality. TVC-GMM reduces spectrogram smoothness and improves perceptual audio quality in particular for expressive datasets as shown by both objective and subjective evaluation.

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