Amyotrophic lateral sclerosis is a fatal disease that not only affects
movement, speech, and breath but also cognition. Recent studies have focused on
the use of language analysis techniques to detect ALS and infer scales for
monitoring functional progression. In this paper, we focused on another
important aspect, cognitive impairment, which affects 35-50% of the ALS
population. In an effort to reach the ALS population, which frequently exhibits
mobility limitations, we implemented the digital version of the Edinburgh
Cognitive and Behavioral ALS Screen (ECAS) test for the first time. This test
which is designed to measure cognitive impairment was remotely performed by 56
participants from the EverythingALS Speech Study. As part of the study,
participants (ALS and non-ALS) were asked to describe weekly one picture from a
pool of many pictures with complex scenes displayed on their computer at home.
We analyze the descriptions performed within +/- 60 days from the day the ECAS
test was administered and extract different types of linguistic and acoustic
features. We input those features into linear regression models to infer 5 ECAS
sub-scores and the total score. Speech samples from the picture description are
reliable enough to predict the ECAS subs-scores, achieving statistically
significant Spearman correlation values between 0.32 and 0.51 for the model's
performance using 10-fold cross-validation.

使用语言分析技术检测肌萎缩侧索硬化症（ALS）患者的认知功能，并预测 ECAS 子分数和总分的线性回归模型的可靠性研究。

远程推断使用图片描述在 ALS 患者中的认知分数

Remote Inference of Cognitive Scores in ALS Patients Using a Picture  Description

We present EMPHASIS, an emotional phoneme-based acoustic model for speech
synthesis system. EMPHASIS includes a phoneme duration prediction model and an
acoustic parameter prediction model. It uses a CBHG-based regression network to
model the dependencies between linguistic features and acoustic features. We
modify the input and output layer structures of the network to improve the
performance. For the linguistic features, we apply a feature grouping strategy
to enhance emotional and prosodic features. The acoustic parameters are
designed to be suitable for the regression task and waveform reconstruction.
EMPHASIS can synthesize speech in real-time and generate expressive
interrogative and exclamatory speech with high audio quality. EMPHASIS is
designed to be a multi-lingual model and can synthesize Mandarin-English speech
for now. In the experiment of emotional speech synthesis, it achieves better
subjective results than other real-time speech synthesis systems.

EMPHASIS 是一种基于情感音素的语音合成系统，使用 CBHG 回归网络来建模语音和声学特征之间的依赖关系，并采用特征分组策略来增强情感和韵律特征，可在实时生成具有高音质的感叹和疑问语调的多种语言（目前支持中英文），在情感语音合成实验中取得了更好的主观结果。