A text-to-speech (TTS) model typically factorizes speech attributes such as
content, speaker and prosody into disentangled representations.Recent works aim
to additionally model the acoustic conditions explicitly, in order to
disentangle the primary speech factors, i.e. linguistic content, prosody and
timbre from any residual factors, such as recording conditions and background
noise.This paper proposes unsupervised, interpretable and fine-grained noise
and prosody modeling. We incorporate adversarial training, representation
bottleneck and utterance-to-frame modeling in order to learn frame-level noise
representations. To the same end, we perform fine-grained prosody modeling via
a Fully Hierarchical Variational AutoEncoder (FVAE) which additionally results
in more expressive speech synthesis.

本文提出了一种基于对抗训练、表示瓶颈和音框级建模的无监督噪音建模方法和基于全层次变分自编码器的细粒度语调建模方法，以更好地进行语音合成并将主要因素与噪声和背景噪声分离。

多说者语音合成的细粒度噪声控制

Fine-grained Noise Control for Multispeaker Speech Synthesis

This paper focuses on unsupervised modeling of morphological families,
collectively comprising a forest over the language vocabulary. This formulation
enables us to capture edgewise properties reflecting single-step morphological
derivations, along with global distributional properties of the entire forest.
These global properties constrain the size of the affix set and encourage
formation of tight morphological families. The resulting objective is solved
using Integer Linear Programming (ILP) paired with contrastive estimation. We
train the model by alternating between optimizing the local log-linear model
and the global ILP objective. We evaluate our system on three tasks: root
detection, clustering of morphological families and segmentation. Our
experiments demonstrate that our model yields consistent gains in all three
tasks compared with the best published results.

本文关注无监督建模形态家族，用整数线性规划 (ILP) 和对比评估解决了此问题，并在三个任务中进行了评估。实验表明，我们的模型在根检测，形态家族聚类和分割等任务方面相较于之前的研究获得了显著进展。