  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.
