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

The Ising model is one of the simplest and most famous models of interacting
systems. It was originally proposed to model ferromagnetic interactions in
statistical physics and is now widely used to model spatial processes in many
areas such as ecology, sociology, and genetics, usually without testing its
goodness of fit. Here, we propose various test statistics and an exact
goodness-of-fit test for the finite-lattice Ising model. The theory of Markov
bases has been developed in algebraic statistics for exact goodness-of-fit
testing using a Monte Carlo approach. However, finding a Markov basis is often
computationally intractable. Thus, we develop a Monte Carlo method for exact
goodness-of-fit testing for the Ising model which avoids computing a Markov
basis and also leads to a better connectivity of the Markov chain and hence to
a faster convergence. We show how this method can be applied to analyze the
spatial organization of receptors on the cell membrane.

本文利用蒙特卡罗方法构建了一种对量子 ISING 模型进行精确拟合度检验的算法，并将其应用于分析细胞膜受体的空间组织。