A Corpus for Large-Scale Phonetic Typology (2020)

Abstract: A major hurdle in data-driven research on typology is having sufficient data in many languages to draw meaningful conclusions. We present VoxClamantis v1.0, the first large-scale corpus for phonetic typology, with aligned segments and estimated phoneme-level labels in 690 readings spanning 635 languages, along with acoustic-phonetic measures of vowels and sibilants. Access to such data can greatly facilitate investigation of phonetic typology at a large scale and across many languages. However, it is non-trivial and computationally intensive to obtain such alignments for hundreds of languages, many of which have few to no resources presently available. We describe the methodology to create our corpus, discuss caveats with current methods and their impact on the utility of this data, and illustrate possible research directions through a series of case studies on the 48 highest-quality readings. Our corpus and scripts are publicly available for non-commercial use at https://voxclamantisproject.github.io .

## introduction understanding the range and limits of crosslinguistic variation is fundamental to the scientific study of language. in speech and particularly phonetic typology, this involves exploring potentially universal tendencies that shape sound systems and govern phonetic structure. such investigation requires access to large amounts of cross-linguistic data. previous cross-linguistic phonetic studies have been limited to a small number of languages with available data @xcite , or have relied on previously reported measures from many studies @xcite @xcite . existing multilingual speech corpora have similar restrictions, with data too limited for many tasks @xcite or approximately 20 to 30 recorded languages @xcite @xcite . the recently developed cmu wilderness corpus @xcite constitutes an exception to this rule with over 600 languages. this makes it the largest and most typologically diverse speech corpus to date. in addition to its coverage, the cmu wilderness corpus is unique in two additional aspects: cleanly recorded, read speech exists for all languages in the corpus, and the same content (modulo translation) exists across all languages. however, this massively multilingual speech corpus is challenging to work with directly. copyright, computational restrictions, and sheer size limit its accessibility. due to copyright restrictions, the audio cannot be directly downloaded with the sentence and phoneme alignments. a researcher would need to download original audio mp3 and text through links to bible.is, then segment these with speech-to-text sentence alignments distributed in @xcite . 1 for phonetic research, subsequently identifying examples of specific phonetic segments in the audio is also a near-essential step for extracting relevant acoustic-phonetic measurements. carrying out this derivative step has allowed us to release a stable-access collection of token-level acoustic-phonetic measures to enable further research. obtaining such measurements requires several processing steps: estimating pronunciations, aligning them to the text, evaluating alignment quality, and finally, extracting phonetic measures. this work is further complicated by the fact that, for a sizable number of these languages, no linguistic resources currently exist (e.g., language-specific pronunciation lexicons). we adapt speech processing methods based on @xcite to accomplish these tasks, though not without noise: in §3.4, we identify three significant caveats when attempting to use our extended corpus for large-scale phonetic studies. we release a comprehensive set of standoff markup of over 400 million labeled segments of continuous speech. 2 for each segment, we provide an estimated phoneme-level label from the x-sampa alphabet, the preceding and following labels, and the start position and duration in the audio. vowels are supplemented with formant measurements, and sibilants with standard measures of spectral shape. we present a series of targeted case studies illustrating the utility of our corpus for large-scale phonetic typology. these studies are motivated by potentially universal principles posited to govern phonetic variation: phonetic dispersion and phonetic uniformity. our studies both replicate known results in the phonetics literature and also present novel findings. importantly, these studies investigate current methodology as well as questions of interest to phonetic typology at a large scale. ## original speech the cmu wilderness corpus @xcite consists of recorded readings of the new testament of the bible in many languages and dialects. following the new testament structure, these data are broken into 27 books, each with a variable number of chapters between 1 and 25. bible chapters contain standardized verses (approximately sentence-level segments); however, the speech is originally split only by chapter. each chapter has an average of 13 minutes of speech for a total of ≈20 hours of speech and text per language. these recordings are clean, read speech with a sampling rate of 16 khz. in most languages, they are non-dramatic readings with a single speaker; in some, they are dramatic multi-speaker readings with additive music. 3 the release from @xcite includes several resources for processing the corpus: scripts to download the original source data from bible.is, 'lexicons' created using grapheme-to-phoneme (g2p) conversion, and scripts to apply their generated sentence alignments, which facilitates downstream language processing tasks, including phoneme alignment. ## the voxclamantis v1.0 corpus our voxclamantis v1.0 corpus is derived from 690 audio readings of the new testament of the bible foot_2 in 635 languages. 5 we mark estimated speech seg-ments labeled with phonemic labels, and phonetic measures for the tokens that are vowels or sibilants. the extraction process is diagrammed in figure 2 . in the sections below, we detail our procedures for extracting labeled audio segments and their phonetic measures, in both high-and low-resource languages. we then outline important caveats to keep in mind when using this corpus. ## phonetic case studies we present two case studies to illustrate the utility of our resource for exploration of crosslinguistic typology. phoneticians have posited several typological principles that may structure phonetic systems. though previous research has provided some indication as to the direction and magnitude of expected effects, many instances of the principles have not yet been explored at scale. our case studies investigate how well they account for cross-linguistic variation and systematicity for our phonetic measures from vowels and sibilants. below we present the data filtering methods for our case studies, followed by an introduction to and evaluation of phonetic dispersion and uniformity. ## directions for future work we hope our corpus may serve as a touchstone for further improvements in phonetic typology research and methodology. here we suggest potential steps forward for known areas ( §3.4) where this corpus could be improved: a sentence alignments were generated using unitran, and could be improved with higherquality g2p and verse-level text segmentation to standardize utterances across languages. 13 the magnitude of this correlation did not change when using hertz (@xmath0.86, @xmath1.001). b consistent and comparable phoneme labels are the ultimate goal. concurrent work on universal phone recognition @xcite addresses this issue through a universal phone inventory constrained by language-specific phoible inventories @xcite . however, free-decoding phones from speech alone is challenging. one exciting possibility is to use the orthography and audio jointly to guide semi-supervised learning of per-language pronunciation lexicons @xcite . c reliable quality assessment for current methods remains an outstanding research question for many languages. for covered languages, using a universal label set to map additional high quality lexicons (e.g., hand-annotated lexicons) to the same label space as ours would enable direct label and alignment assessment through precision, recall, and per. d curating additional resources beyond this corpus would improve coverage and balance, such as contributing additional epitran modules. additional readings exist for many languages on the original bible.is site and elsewhere. annotations with speaker information are not available, but improved unsupervised speaker clustering may also support better analysis. ## conclusion voxclamantis v1.0 is the first large-scale corpus for phonetic typology, with extracted phonetic features for 635 typologically diverse languages. we present two case studies illustrating both the research potential and limitations of this corpus for investigation of phonetic typology at a large scale. we discuss several caveats for the use of this corpus and areas for substantial improvement. nonetheless, we hope that directly releasing our alignments and token-level features enables greater research accessibility in this area. we hope this corpus will motivate and enable further developments in both phonetic typology and methodology for working with cross-linguistic speech corpora.