This paper introduces a novel combination of two tasks, previously treated
separately: acoustic-to-articulatory speech inversion (AAI) and
phoneme-to-articulatory (PTA) motion estimation. We refer to this joint task as
acoustic phoneme-to-articulatory speech inversion (APTAI) and explore two
different approaches, both working speaker- and text-independently during
inference. We use a multi-task learning setup, with the end-to-end goal of
taking raw speech as input and estimating the corresponding articulatory
movements, phoneme sequence, and phoneme alignment. While both proposed
approaches share these same requirements, they differ in their way of achieving
phoneme-related predictions: one is based on frame classification, the other on
a two-staged training procedure and forced alignment. We reach competitive
performance of 0.73 mean correlation for the AAI task and achieve up to
approximately 87% frame overlap compared to a state-of-the-art text-dependent
phoneme force aligner.

引入了一种新的方法，将声学到口腔运动的转换和音素到口腔运动估计两项任务结合起来，称之为声学音素到口腔运动的反演。探索了两种不同的方法，在推理过程中都采用与说话人和文本无关的方式。使用多任务学习的模式，以端到端的目标将原始语音作为输入，估计相应的口腔运动、音素序列和音素对齐。两种方法在音素相关预测方面有所不同，一个基于帧分类，另一个采用两阶段训练过程和强制对齐。在声学到口腔运动转换任务中获得了 0.73 的平均相关性，并与现有的依赖于文本的音素强制对齐器相比，实现了高达 87% 的帧重叠。

从语音中独立估计发音器官运动和音位对齐

Speaker- and Text-Independent Estimation of Articulatory Movements and  Phoneme Alignments from Speech

One of the burning problems lately in Japan has been cyber-bullying, or
slandering and bullying people online. The problem has been especially noticed
on unofficial Web sites of Japanese schools. Volunteers consisting of school
personnel and PTA (Parent-Teacher Association) members have started Online
Patrol to spot malicious contents within Web forums and blogs. In practise,
Online Patrol assumes reading through the whole Web contents, which is a task
difficult to perform manually. With this paper we introduce a research intended
to help PTA members perform Online Patrol more efficiently. We aim to develop a
set of tools that can automatically detect malicious entries and report them to
PTA members. First, we collected cyber-bullying data from unofficial school Web
sites. Then we performed analysis of this data in two ways. Firstly, we
analysed the entries with a multifaceted affect analysis system in order to
find distinctive features for cyber-bullying and apply them to a machine
learning classifier. Secondly, we applied a SVM based machine learning method
to train a classifier for detection of cyber-bullying. The system was able to
classify cyber-bullying entries with 88.2% of balanced F-score.

该研究旨在帮助家长教师会成员更有效地对网络论坛和博客的恶意内容进行监测，以实际应用机器学习等技术进行网络欺凌入口的分类和识别。通过对不同学校的网络平台数据进行的研究，该研究成功训练了机器学习分类器，最终分类准确率达到 88.2% 的平衡 F 分数。