标题： 通过建模残差多模态实现稳健的FastSpeech 2 显示英文标题

标题： Towards Robust FastSpeech 2 by Modelling Residual Multimodality

摘要： 基于FastSpeech 2的最新非自回归文本到语音(TTS)模型可以高效地合成高质量和自然的语音。然而，对于富有表现力的语音数据集，我们观察到特征性的音频失真。我们证明，这些伪影是由于使用均方误差(MSE)损失训练频谱图解码器时，对频谱图预测进行了过度平滑，从而引入到声码器重建中的。使用MSE损失的FastSpeech 2仅能学习训练分布的条件平均值，如果在所有条件信号之后分布仍然呈现多模态，则这些平均值可能远离自然样本。为缓解此问题，我们引入了TVC-GMM，这是一种三元链高斯分布的混合模型，用于建模残余多模态性。TVC-GMM减少了频谱图的平滑度，并通过客观和主观评估显示，特别是在富有表现力的数据集上，提高了感知音频质量。 显示英文摘要

摘要： 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.