This study explores F0 entrainment in second language (L2) English speech
imitation during an Alternating Reading Task (ART). Participants with Italian,
French, and Slovak native languages imitated English utterances, and their F0
entrainment was quantified using the Dynamic Time Warping (DTW) distance
between the parameterized F0 contours of the imitated utterances and those of
the model utterances. Results indicate a nuanced relationship between L2
English proficiency and entrainment: speakers with higher proficiency generally
exhibit less entrainment in pitch variation and declination. However, within
dyads, the more proficient speakers demonstrate a greater ability to mimic
pitch range, leading to increased entrainment. This suggests that proficiency
influences entrainment differently at individual and dyadic levels,
highlighting the complex interplay between language skill and prosodic
adaptation.

这项研究探讨了第二语言（L2）英语演讲模仿中 F0 演化的问题，在交替阅读任务（ART）中。结果表明，L2 英语熟练程度与演化之间存在微妙的关系：熟练程度较高的演讲者在音高变化和下降方面通常表现出较少的演化。然而，在一对一的情况下，熟练程度较高的演讲者表现出更强的模仿音高范围的能力，导致进一步的演化。这表明熟练程度在个体和对位级别上对演化产生不同的影响，突出了语言技能和韵律的复杂相互作用。

语言熟练度与 F0 同步：对意大利、法国和斯洛伐克母语者进行 L2 英语模仿的研究

Language Proficiency and F0 Entrainment: A Study of L2 English Imitation  in Italian, French, and Slovak Speakers

Speech Language Models (SLMs) aim to learn language from raw audio, without
textual resources. Despite significant advances, our current models exhibit
weak syntax and semantic abilities. However, if the scaling properties of
neural language models hold for the speech modality, these abilities will
improve as the amount of compute used for training increases. In this paper, we
use models of this scaling behavior to estimate the scale at which our current
methods will yield a SLM with the English proficiency of text-based Large
Language Models (LLMs). We establish a strong correlation between pre-training
loss and downstream syntactic and semantic performance in SLMs and LLMs, which
results in predictable scaling of linguistic performance. We show that the
linguistic performance of SLMs scales up to three orders of magnitude more
slowly than that of text-based LLMs. Additionally, we study the benefits of
synthetic data designed to boost semantic understanding and the effects of
coarser speech tokenization.

本文使用语言模型的扩展性行为来估计目前方法在训练中使用的计算规模下，会产生具有类似基于文本的大型语言模型的英语熟练度的语音语言模型。我们展示了语言模型和大型语言模型的预训练损失与下游句法和语义性能之间存在强相关性，从而导致语言性能的可预测扩展。我们还表明，与基于文本的大型语言模型相比，语音语言模型的语言性能扩展速度慢三个量级。此外，我们研究了旨在提升语义理解的合成数据的益处以及更粗糙的语音分词的影响。

语音语言模型的规模特性

Scaling Properties of Speech Language Models

We highlight the challenges faced by non-native speakers when using AI
writing assistants to paraphrase text. Through an interview study with 15
non-native English speakers (NNESs) with varying levels of English proficiency,
we observe that they face difficulties in assessing paraphrased texts generated
by AI writing assistants, largely due to the lack of explanations accompanying
the suggested paraphrases. Furthermore, we examine their strategies to assess
AI-generated texts in the absence of such explanations. Drawing on the needs of
NNESs identified in our interview, we propose four potential user interfaces to
enhance the writing experience of NNESs using AI writing assistants. The
proposed designs focus on incorporating explanations to better support NNESs in
understanding and evaluating the AI-generated paraphrasing suggestions.

本研究通过对 15 个英语水平各异的非母语英语人士的访谈研究，发现他们在使用 AI 写作助手进行改写时往往无法正确评估其生成的文本，主要由于缺乏解释。为此，我们提出了四种用户界面设计，旨在通过提供解释来支持非母语英语人士更好地理解和评估 AI 生成的改写建议。