Research in speech technologies and comparative linguistics depends on access
to diverse and accessible speech data. The UCLA Phonetics Lab Archive is one of
the earliest multilingual speech corpora, with long-form audio recordings and
phonetic transcriptions for 314 languages (Ladefoged et al., 2009). Recently,
95 of these languages were time-aligned with word-level phonetic transcriptions
(Li et al., 2021). Here we present VoxAngeles, a corpus of audited phonetic
transcriptions and phone-level alignments of the UCLA Phonetics Lab Archive,
which uses the 95-language CMU re-release as our starting point. VoxAngeles
also includes word- and phone-level segmentations from the original UCLA
corpus, as well as phonetic measurements of word and phone durations, vowel
formants, and vowel f0. This corpus enhances the usability of the original
data, particularly for quantitative phonetic typology, as demonstrated through
a case study of vowel intrinsic f0. We also discuss the utility of the
VoxAngeles corpus for general research and pedagogy in crosslinguistic
phonetics, as well as for low-resource and multilingual speech technologies.
VoxAngeles is free to download and use under a CC-BY-NC 4.0 license.

UCLA Phonetics Lab Archive 中的语音技术和比较语言学研究依赖于丰富且易于获取的语音数据。通过提供 VoxAngeles，我们提供了 UCLA Phonetics Lab Archive 的经过审核的语音转录和音素级对齐的语料库，该语料库以 95 种语言的 CMU 重新发行版本为起点，通过案例研究说明了该语料库在定量音位类型学方面的可用性，还讨论了 VoxAngeles 语料库在跨语言音韵学、资源匮乏和多语言语音技术方面的实用性。

UCLA 音韵实验室档案的音标分割

Phonetic Segmentation of the UCLA Phonetics Lab Archive

In this paper, we present a methodology for linguistic feature extraction,
focusing particularly on automatically syllabifying words in multiple
languages, with a design to be compatible with a forced-alignment tool, the
Montreal Forced Aligner (MFA). In both the textual and phonetic domains, our
method focuses on the extraction of phonetic transcriptions from text, stress
marks, and a unified automatic syllabification (in text and phonetic domains).
The system was built with open-source components and resources. Through an
ablation study, we demonstrate the efficacy of our approach in automatically
syllabifying words from several languages (English, French and Spanish).
Additionally, we apply the technique to the transcriptions of the CMU ARCTIC
dataset, generating valuable annotations available
online\footnote{https://github.com/noetits/MUST_P-SRL} that are ideal for
speech representation learning, speech unit discovery, and disentanglement of
speech factors in several speech-related fields.

本文介绍了一种语言特征提取的方法，特别关注多种语言中单词的自动音节划分，在文本和音标领域中提取音素转录、重音标记和统一的自动音节划分；通过剔除研究，我们证明了这种方法在多种语言（英语、法语和西班牙语）中自动音节划分单词的有效性；此外，我们将该技术应用于 CMU ARCTIC 数据集的转录中，生成了有价值的注释，可供语音表示学习、语音单元发现和语音相关领域中的语音要素分离使用。

MUST&P-SRL：跨多语言和单一音节划分的文本和语音表示学习

MUST&P-SRL: Multi-lingual and Unified Syllabification in Text and  Phonetic Domains for Speech Representation Learning

Variation in speech is often quantified by comparing phonetic transcriptions
of the same utterance. However, manually transcribing speech is time-consuming
and error prone. As an alternative, therefore, we investigate the extraction of
acoustic embeddings from several self-supervised neural models. We use these
representations to compute word-based pronunciation differences between
non-native and native speakers of English, and between Norwegian dialect
speakers. For comparison with several earlier studies, we evaluate how well
these differences match human perception by comparing them with available human
judgements of similarity. We show that speech representations extracted from a
specific type of neural model (i.e. Transformers) lead to a better match with
human perception than two earlier approaches on the basis of phonetic
transcriptions and MFCC-based acoustic features. We furthermore find that
features from the neural models can generally best be extracted from one of the
middle hidden layers than from the final layer. We also demonstrate that neural
speech representations not only capture segmental differences, but also
intonational and durational differences that cannot adequately be represented
by a set of discrete symbols used in phonetic transcriptions.

研究使用自我监督的神经模型从语音中提取声学嵌入，计算非英语母语和英语母语以及挪威方言发音之间的基于单词的差异，并发现使用转换器的神经模型提取的语音表示与人类感知匹配得更好。