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.

In the mid 2000s, there were several large-scale US National Science Foundation (NSF) grants awarded to projects aiming at developing digital infrastructure and standards for different forms of linguistics data. For example, MultiTree encoded language family trees as phylogenies in XML and LL-MAP converted detailed geographic maps of endangered languages into KML. As early stand-alone website applications, these projects allowed researchers interested in comparative linguistics to explore language genealogies and areality, respectively. However as time passed, the technologies that supported these web apps became deprecated, unsupported, and inaccessible. Here we take a future-oriented approach to digital obsolescence and illustrate how to convert legacy linguistic resources into FAIR data via the Cross-Linguistic Data Formats (CLDF). CLDF is built on the W3C recommendations Model for Tabular Data and Metadata on the Web and Metadata Vocabulary for Tabular Data developed by the CSVW (CSV on the Web) working group. Thus, each dataset is modeled as a set of tabular data files described by metadata in JSON. These standards and the tools built to validate and manipulate them provide an accessible and extensible format for converting legacy linguistic web apps into FAIR datasets.

This study presents a temporal co-registration method combining electromagnetic articulography (EMA) and electroencephalography (EEG) to capture the neural planning and execution phases of speech with high precision. Traditional EEG alignment based on acoustic vocal onset is often inaccurate due to the variable lag between articulatory and acoustic onsets. Our approach synchronizes EMA-derived speech kinematics with EEG data, addressing these challenges. We also examined the interaction between EMA and EEG systems, focusing on the integrity of EMA signals in the presence of EEG equipment and the electromagnetic influence of EMA on EEG signal quality. The method achieved a mean alignment delay of 2.7 ms (SD = 0.4 ms), enabling detailed analysis of pre-articulatory brain activities. Additionally, our evaluations confirmed the robustness of EMA signals and EEG event-related potentials, supporting the method's precision, feasibility, and reliability for speech planning research.

We present the TeDDi sample, a diversity sample of text data for language comparison and multilingual Natural Language Processing. The TeDDi sample currently features 89 languages based on the typological diversity sample in the World Atlas of Language Structures. It consists of more than 20k texts and is accompanied by open-source corpus processing tools. The aim of TeDDi is to facilitate text-based quantitative analysis of linguistic diversity. We describe in detail the TeDDi sample, how it was created, data availability, and its added value through for NLP and linguistic research.

We present the ACQDIV corpus database and aggregation pipeline, a tool developed as part of the European Research Council (ERC) funded project ACQDIV, which aims to identify the universal cognitive processes that allow children to acquire any language. The corpus database represents 15 corpora from 14 typologically maximally diverse languages. Here we give an overview of the project, database, and our extensible software package for adding more corpora to the current language sample. Lastly, we discuss how we use the corpus database to mine for universal patterns in child language acquisition corpora and we describe avenues for future research.

When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as unsupervised bottom-up segmentation.Proposed strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language.We report on the stability in performance of 11 conceptually diverse algorithms on a selection of 8 typologically distinct languages.The results are evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.