In this paper, we present a novel method for phoneme-level prosody control of
F0 and duration using intuitive discrete labels. We propose an unsupervised
prosodic clustering process which is used to discretize phoneme-level F0 and
duration features from a multispeaker speech dataset. These features are fed as
an input sequence of prosodic labels to a prosody encoder module which augments
an autoregressive attention-based text-to-speech model. We utilize various
methods in order to improve prosodic control range and coverage, such as
augmentation, F0 normalization, balanced clustering for duration and
speaker-independent clustering. The final model enables fine-grained
phoneme-level prosody control for all speakers contained in the training set,
while maintaining the speaker identity. Instead of relying on reference
utterances for inference, we introduce a prior prosody encoder which learns the
style of each speaker and enables speech synthesis without the requirement of
reference audio. We also fine-tune the multispeaker model to unseen speakers
with limited amounts of data, as a realistic application scenario and show that
the prosody control capabilities are maintained, verifying that the
speaker-independent prosodic clustering is effective. Experimental results show
that the model has high output speech quality and that the proposed method
allows efficient prosody control within each speaker's range despite the
variability that a multispeaker setting introduces.

本文介绍一种使用直观的离散标签实现音素级 F0 和时长控制的新方法，其使用无监督的韵律聚类过程将音素级 F0 和时长特征离散化为韵律标签的输入序列，该模型不需要参考语音就能合成语音，并具有高质量的语音输出和有效的韵律控制能力。

通过学习离散音素级韵律表示实现可控语音合成

Controllable speech synthesis by learning discrete phoneme-level prosodic representations

During the outbreak of COVID-19 pandemic, several research areas joined
efforts to mitigate the damages caused by SARS-CoV-2. In this paper we present
an interpretability analysis of a convolutional neural network based model for
COVID-19 detection in audios. We investigate which features are important for
model decision process, investigating spectrograms, F0, F0 standard deviation,
sex and age. Following, we analyse model decisions by generating heat maps for
the trained models to capture their attention during the decision process.
Focusing on a explainable Inteligence Artificial approach, we show that studied
models can taken unbiased decisions even in the presence of spurious data in
the training set, given the adequate preprocessing steps. Our best model has
94.44% of accuracy in detection, with results indicating that models favors
spectrograms for the decision process, particularly, high energy areas in the
spectrogram related to prosodic domains, while F0 also leads to efficient
COVID-19 detection.

本研究使用卷积神经网络检测 COVID-19，使用谱图和 F0 等特征解释神经网络判定过程，生成热图来捕捉模型决策过程，结果显示谱图相关能量高的区域对决策过程起主导作用，同时 F0 也有帮助。研究表明，即使在训练集中存在虚假数据的情况下，所研究的模型也可以做出无偏差的决策，为基于解释性人工智能的研究提供了参考。

COVID-19 检测深度模型可解释性分析

Interpretability Analysis of Deep Models for COVID-19 Detection

Emotional voice conversion aims to convert the emotion of speech from one
state to another while preserving the linguistic content and speaker identity.
The prior studies on emotional voice conversion are mostly carried out under
the assumption that emotion is speaker-dependent. We consider that there is a
common code between speakers for emotional expression in a spoken language,
therefore, a speaker-independent mapping between emotional states is possible.
In this paper, we propose a speaker-independent emotional voice conversion
framework, that can convert anyone's emotion without the need for parallel
data. We propose a VAW-GAN based encoder-decoder structure to learn the
spectrum and prosody mapping. We perform prosody conversion by using continuous
wavelet transform (CWT) to model the temporal dependencies. We also investigate
the use of F0 as an additional input to the decoder to improve emotion
conversion performance. Experiments show that the proposed speaker-independent
framework achieves competitive results for both seen and unseen speakers.

本文提出了一个说话人无关的情感语音转换框架，该框架使用基于 VAW-GAN 的编码器 - 解码器结构和连续小波变换（CWT）来执行谱和韵律转换，还尝试使用 F0 作为解码器的附加输入来提高情感转换性能。实验结果表明，该框架在看到和未看到的说话人方面都可以获得有竞争力的结果。