Human movement studies and analyses have been fundamental in many scientific
domains, ranging from neuroscience to education, pattern recognition to
robotics, health care to sports, and beyond. Previous speech motor models were
proposed to understand how speech movement is produced and how the resulting
speech varies when some parameters are changed. However, the inverse approach,
in which the muscular response parameters and the subject's age are derived
from real continuous speech, is not possible with such models. Instead, in the
handwriting field, the kinematic theory of rapid human movements and its
associated Sigma-lognormal model have been applied successfully to obtain the
muscular response parameters. This work presents a speech kinematics based
model that can be used to study, analyze, and reconstruct complex speech
kinematics in a simplified manner. A method based on the kinematic theory of
rapid human movements and its associated Sigma lognormal model are applied to
describe and to parameterize the asymptotic impulse response of the
neuromuscular networks involved in speech as a response to a neuromotor
command. The method used to carry out transformations from formants to a
movement observation is also presented. Experiments carried out with the
(English) VTR TIMIT database and the (German) Saarbrucken Voice Database,
including people of different ages, with and without laryngeal pathologies,
corroborate the link between the extracted parameters and aging, on the one
hand, and the proportion between the first and second formants required in
applying the kinematic theory of rapid human movements, on the other. The
results should drive innovative developments in the modeling and understanding
of speech kinematics.

基于语音运动学理论，引用 Sigma-lognormal 模型，提出了一种用于研究、分析和重构复杂语音运动学的简化模型，通过从格式到运动观察的转换，以及实验证实了抽取参数与人的年龄以及动态运动学理论所要求的第一和第二共振峰之间的比例的相关性。

语音的 Sigma-lognormal 建模

Sigma-lognormal modeling of speech

In this paper, we propose a new method for the accurate estimation and
tracking of formants in speech signals using time-varying quasi-closed-phase
(TVQCP) analysis. Conventional formant tracking methods typically adopt a
two-stage estimate-and-track strategy wherein an initial set of formant
candidates are estimated using short-time analysis (e.g., 10--50 ms), followed
by a tracking stage based on dynamic programming or a linear state-space model.
One of the main disadvantages of these approaches is that the tracking stage,
however good it may be, cannot improve upon the formant estimation accuracy of
the first stage. The proposed TVQCP method provides a single-stage formant
tracking that combines the estimation and tracking stages into one. TVQCP
analysis combines three approaches to improve formant estimation and tracking:
(1) it uses temporally weighted quasi-closed-phase analysis to derive
closed-phase estimates of the vocal tract with reduced interference from the
excitation source, (2) it increases the residual sparsity by using the $L_1$
optimization and (3) it uses time-varying linear prediction analysis over long
time windows (e.g., 100--200 ms) to impose a continuity constraint on the vocal
tract model and hence on the formant trajectories. Formant tracking experiments
with a wide variety of synthetic and natural speech signals show that the
proposed TVQCP method performs better than conventional and popular formant
tracking tools, such as Wavesurfer and Praat (based on dynamic programming),
the KARMA algorithm (based on Kalman filtering), and DeepFormants (based on
deep neural networks trained in a supervised manner). Matlab scripts for the
proposed method can be found at: this https URL

本文提出了一种新的方法，使用时间变化的准闭相位（TVQCP）分析准确估计和跟踪语音信号中的共振峰。

语音信号准确共振峰跟踪的时变准闭相位分析

Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking  in Speech Signals

Self-supervised representations of speech are currently being widely used for
a large number of applications. Recently, some efforts have been made in trying
to analyze the type of information present in each of these representations.
Most such work uses downstream models to test whether the representations can
be successfully used for a specific task. The downstream models, though,
typically perform nonlinear operations on the representation extracting
information that may not have been readily available in the original
representation. In this work, we analyze the spatial organization of phone and
speaker information in several state-of-the-art speech representations using
methods that do not require a downstream model. We measure how different layers
encode basic acoustic parameters such as formants and pitch using
representation similarity analysis. Further, we study the extent to which each
representation clusters the speech samples by phone or speaker classes using
non-parametric statistical testing. Our results indicate that models represent
these speech attributes differently depending on the target task used during
pretraining.

本文分析了几种最先进的自监督语音表示中的电话和说话者信息的空间组织，并使用表示相似性分析测量不同层如何编码基本声学参数，例如共振峰和音高，进一步使用非参数统计测试研究每个表示在电话或说话者类别上进行聚集的程度，结果表明，模型根据预训练时使用的目标任务不同而以不同的方式表示这些语音属性。

自监督语音表示中的手机和扬声器空间组织

Phone and speaker spatial organization in self-supervised speech representations

Infant-directed speech (IDS) has distinctive properties that differ from
adult-directed speech (ADS). Why it has these properties -- and whether they
are intended to facilitate language learning -- is matter of contention. We
argue that much of this disagreement stems from lack of a formal, guiding
theory of how phonetic categories should best be taught to infant-like
learners. In the absence of such a theory, researchers have relied on
intuitions about learning to guide the argument. We use a formal theory of
teaching, validated through experiments in other domains, as the basis for a
detailed analysis of whether IDS is well-designed for teaching phonetic
categories. Using the theory, we generate ideal data for teaching phonetic
categories in English. We qualitatively compare the simulated teaching data
with human IDS, finding that the teaching data exhibit many features of IDS,
including some that have been taken as evidence IDS is not for teaching. The
simulated data reveal potential pitfalls for experimentalists exploring the
role of IDS in language learning. Focusing on different formants and phoneme
sets leads to different conclusions, and the benefit of the teaching data to
learners is not apparent until a sufficient number of examples have been
provided. Finally, we investigate transfer of IDS to learning ADS. The teaching
data improves classification of ADS data, but only for the learner they were
generated to teach; not universally across all classes of learner. This
research offers a theoretically-grounded framework that empowers
experimentalists to systematically evaluate whether IDS is for teaching.

使用一种正式的教学理论，将诱导婴儿语（IDS）与从理论上生成的教学数据进行比较分析，研究了 IDS 如何教授语音类别以及 IDS 如何影响成人语音的学习和表征。