Phonetic Segmentation of the UCLA Phonetics Lab Archive
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 5 of its 28 references that are in the map. First posted on arXiv 28 Mar 2024.
Builds on
5 of 28 references.
References · 5
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
- 2020MLS: A Large-Scale Multilingual Dataset for Speech Researchcited by 467
- 2020A Corpus for Large-Scale Phonetic Typologycited by 9
- 2022Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognitioncited by 7
- 2021What shall we do with an hour of data? Speech recognition for the un- and under-served languages of Common Voicecited by 5
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Citing papers per year: 2024: 1; 2025: 0; 2026: 8.
9 papers.
- 2026[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmeticcited by 11
- 2026Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspacescited by 7
- 2026An Empirical Recipe for Universal Phone Recognitioncited by 5
- 2026PRiSM: Benchmarking Phone Realization in Speech Modelscited by 4
- 2026HuPER: A Human-Inspired Framework for Phonetic Perceptioncited by 3
- 2024Efficiently Identifying Low-Quality Language Subsets in Multilingual Datasets: A Case Study on a Large-Scale Multilingual Audio Datasetcited by 1
- 2026Phone Segmentation and Recognition through Phonological Activation Mappingcited by 0
- 2026FuseAlign: Forced Alignment in the Wildcited by 0
- 2026Reconstructing the Vocal Tract with Differentiable Acoustic Simulationcited by 0
- 2026Reconstructing the Vocal Tract with Differentiable Acoustic Simulation29 Sep 2026
- 2026FuseAlign: Forced Alignment in the Wild27 Sep 2026
- 2026Phone Segmentation and Recognition through Phonological Activation Mapping10 Jul 2026
- 2026An Empirical Recipe for Universal Phone Recognition30 Mar 2026
- 2026Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces13 Mar 2026
- 2026[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic21 Feb 2026
- 2026HuPER: A Human-Inspired Framework for Phonetic Perception2 Feb 2026
- 2026PRiSM: Benchmarking Phone Realization in Speech Models20 Jan 2026
- 2024Efficiently Identifying Low-Quality Language Subsets in Multilingual Datasets: A Case Study on a Large-Scale Multilingual Audio Dataset5 Oct 2024