PosterPDF Available
Languages with
simultaneous
secondary articulations
Towards a typology of secondary articulations
Philipp Buech, Anne Hermes, Rachid Ridouane
Laboratoire de Phonétique et Phonologie UMR 7018 (CNRS/Sorbonne Nouvelle, Paris)
Methods
Datasets: PHOIBLE (Moran & McCloy, 2019) & GLOTTOLOG (Hammarström et al., 2022)
Data basis: One randomly sampled phoneme inventory for each Glottocode in PHOIBLE
( = 2,177 phoneme inventories)
Python v3.10 (Van Rossum & Drake, 2009), Cartopy v0.20.2 (Met Office, 2015), PyMC3
v3.11.2
(Salvatier et al., 2016),Bambi v0.7.1 (Capretto et al., 2020)
Phonemes were grouped into Place according to the major category of their transcription;
Manner groupings were done partly according to Hayes (2009); Voicing according to
the feature periodicGlottalSource] (ambiguous values, e.g., "+,-", "-,+" were ignored)
Overview
Figure 2. Number of phoneme inventories by
secondary articulation.
Figure 3. Heatmap of the number of phoneme
inventories with two secondary articulations.
Place Manner
For detailed
analyses see:
Feature
-based principles
PLACE: Set of consonants with a secondary articulation
isinfe
rior to the set of their plain counterparts (Fig. 8)
MANNER: Secondary articulation most likely in plosives,
most unlikely in affricates and approximants (Fig. 6);
Most phoneme inventories have labialized and palatalized
stops; pharyngealization occurs in plosives and fricatives
(almost) alike
VOICING: The overwhelming majority of the phoneme
inventories has secondary articulations on obstruents. More
languages have secondary articulations on voiceless
obstruents
than on voiced obstruents (Fig. 7)
When
Feature-Economy counteracts Markedness:
Labialization in Tashlhiyt, Rutul, Archi, etc. (Fig. 8)
See also the case of pharyngealization in Tashlhiyt with
its
sixteen pharyngealized consonants.
Marked
Feature Avoidance (Clements 2009):
Negative correlation: segments with secondary
articulation occur in fewer inventories (Fig. 4)
Positive correlation: marked segments occur in larger
inventories: phoneme inventories with 3 sec art (62) >
phoneme inventories with 2 sec art (≈39) > languages
with
1 sec art (≈27) > phoneme inventories with 0 sec art (≈22
)
References
Capretto
, T., Piho, C., Kumar, R., Westfall, J., Yarkoni, T., & Martin, O. A. (2020). Bambi: A simple interface for fitting bayesian linear models in python. Retrieved May 2, 2022, from
bambinos.github.io/
bambi/ |Gordon, M. K. (2016). Phonological typology. Oxford University Press. |Hammarstm, H., Forkel, R., Haspelmath, M., & Bank, S. (2022). Glottolog 4.5. Leipzig: Max
Planck
Institute for Evolutionary Anthropology. Retrieved May 16, 2022, from www.glottolog.org |Hayes, B. (2009). Introductory Phonology. Wiley-Blackwell. |Maddieson, I. (1984). Patterns of sounds
.
Cambridge University Press.
|Met Office. (2015). Cartopy: A cartographic python library with a matplotlib interface. http://scitools.org.uk/cartopy |Moran, S., & McCloy, D. (2019). PHOIBLE 2.0.
Retrieved
May 16, 2022, from https://github.com/phoible/dev |Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. (2016). Probabilistic programming in python using pymc3. PeerJ Computer Science, 2, 124.
https://doi.org/10.7717/peerj
-cs.55 |Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. CreateSpace.
Voicing
Figure 5. Number of phoneme inventories by secondary
articulation and place.
Figure 8. Scatterplot and regression
estimates of the number of labialized
dorsal consonants by plain dorsal
consonants (with added jitter) with 95%
HDI. Phoneme inventories falling outside
the 95%HDI are marked with their
language name.
Figure 7. Number of phoneme inventories by
secondary articulation and
sonorant/obstruent (top) and voicing
distinction (bottom).
Figure 4. Mean consonant inventory size (blue) and
number of inventories (red) by number of secondary
articulations.
Figure 6. Number of phoneme inventories by secondary
articulation and manner.
Introduction
Maddieson (1984) with some reference to secondary
articulations in about 317 languages
No address of secondary articulations in
other studies (e.g., Gordon, 2016)
Research aim: Quantitative typological overview of
labialization, palatalization, pharyngealization, and
velarization using a large dataset
General
observations
~ 23% (502/2,177) of the phoneme
inventories have at least one secondary
articulation
Order by frequency: labialization >
palatalization > pharyngealization >
velarization (Fig. 2)
If a phoneme inventory has two
secondary articulations, labialization
and
palatalization pattern most often
together
(Fig. 3)
If a phoneme inventory has
simultaneous
secondary articulations (e.g., /mʷˠ/ in
Satawalese), labialization is always one
of them:
Labialization + Palatalization (N=5)
Labialization + Pharyngealization (N=3)
Labialization + Velarization (N=2)
Languages with 2
secondary articulations Languages with 3
secondary articulations
Labialization Palatalization Pharyngealization Velarization
Geographical distribution
Figure 1. Geographical distribution of secondary articulations.
Article
Full-text available
This study investigated the implementation of pharyngealization in Tashlhiyt, across various linguistic contexts VCV, VCCV, VCCV, and VCCCV. We analyzed articulatory and acoustic data from six male speakers who produced real words containing these sequences with both plain ([d, z]) and pharyngealized target consonants ([dˤ, zˤ]). The investigation comprised dynamic analyses of kinematic and formant trajectories, as well as acoustic parameters of consonants and intrusive schwas. While pharyngealization did not affect the tongue tip, it did lead to a significant lowering of the tongue body. This lowering was not confined solely to the target consonants but was observed in larger domains, extending up to nearly the entire VCCCV items. The primary acoustic correlate of pharyngealization was identified as a lowered F2 of vowels and intrusive schwas, while acoustic properties of consonants remained unaffected. Consistent with observations in the articulatory domain, the lowering of F2 was not restricted solely to vowels adjacent to pharyngealized coronals; instead, it extended to larger domains, even when there were intervening consonants.
Article
Full-text available
Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source PP framework with an intutive and readable, yet powerful, syntax that is close to the natural syntax statisticians use to describe models. It features next-generation Markov chain Monte Carlo (MCMC) sampling algorithms such as the No-U-Turn Sampler (NUTS; Hoffman, 2014), a self-tuning variant of Hamiltonian Monte Carlo (HMC; Duane, 1987). Probabilistic programming in Python confers a number of advantages including multi-platform compatibility, an expressive yet clean and readable syntax, easy integration with other scientific libraries, and extensibility via C, C++, Fortran or Cython. These features make it relatively straightforward to write and use custom statistical distributions, samplers and transformation functions, as required by Bayesian analysis.
Glottolog 4.5. Leipzig: Max Planck Institute for Evolutionary Anthropology
  • T Capretto
  • C Piho
  • R Kumar
  • J Westfall
  • T Yarkoni
  • O A Martin
  • H Hammarström
  • R Forkel
  • M Haspelmath
  • S Bank
Capretto, T., Piho, C., Kumar, R., Westfall, J., Yarkoni, T., & Martin, O. A. (2020). Bambi: A simple interface for fitting bayesian linear models in python. Retrieved May 2, 2022, from bambinos.github.io/bambi/ | Gordon, M. K. (2016). Phonological typology. Oxford University Press. | Hammarström, H., Forkel, R., Haspelmath, M., & Bank, S. (2022). Glottolog 4.5. Leipzig: Max Planck Institute for Evolutionary Anthropology. Retrieved May 16, 2022, from www.glottolog.org | Hayes, B. (2009). Introductory Phonology. Wiley-Blackwell. | Maddieson, I. (1984). Patterns of sounds.