Biology matters: Variation in vocal tract anatomy and language

There are about 7,000 or so languages currently used, and they vary in myriad ways at all their levels. We argue here that part of this cross-linguistic diversity might be explained by factors that are external to language itself, but which differ between groups of speakers and to which language adapts. In particular, we present evidence that there is widespread variation between individuals and groups in what concerns the anatomy of the vocal tract, variation that results in biases (that generate constraints and affordances) which may affect phonetics and phonology. We propose that factors such as the frequency of the biased speakers, their status and position in the communicative network of a speech community form a pool of standing variation which interacts in complex ways with the community’s language and may result in the community-wide amplification of such biases. While more work is necessary, we suggest that these processes play a role in explaining the observed linguistic diversity

Pushes and pulls from below: Anatomical variation, articulation and sound change

Contains fulltext : 200881.pdf (publisher's version ) (Open Access)33 p

Modelling human hard palate shape with Bézier curves

<div><p>People vary at most levels, from the molecular to the cognitive, and the shape of the hard palate (the bony roof of the mouth) is no exception. The patterns of variation in the hard palate are important for the forensic sciences and (palaeo)anthropology, and might also play a role in speech production, both in pathological cases and normal variation. Here we describe a method based on Bézier curves, whose main aim is to generate possible shapes of the hard palate in humans for use in computer simulations of speech production and language evolution. Moreover, our method can also capture existing patterns of variation using few and easy-to-interpret parameters, and fits actual data obtained from MRI traces very well with as little as two or three free parameters. When compared to the widely-used Principal Component Analysis (PCA), our method fits actual data slightly worse for the same number of degrees of freedom. However, it is much better at generating new shapes without requiring a calibration sample, its parameters have clearer interpretations, and their ranges are grounded in geometrical considerations.</p></div

Cubic Bezier curves with lower and higher spatial sampling intervals.

<p>The left panel <b>(a)</b> shows , while the right panel <b>(b)</b> shows (control points are not shown).</p

A High-Speed Laryngoscopic Investigation of Aryepiglottic Trilling

International audienceSix aryepiglottic trills with varied laryngeal parameters were recorded using high-speed laryngoscopy to investigate the nature of the oscillatory behavior of the upper margin of the epilaryngeal tube. Image analysis techniques were applied to extract data about the patterns of aryepiglottic fold oscillation, with a focus on the oscillatory frequencies of the folds. The acoustic impact of aryepiglottic trilling is also considered, along with possible interactions between the aryepiglottic vibration and vocal fold vibration during the voiced trill. Overall, aryepiglottic trilling is deemed to be correctly labeled as a trill in phonetic terms, while also acting as a means to alter the quality of voicing to be auditorily harsh. In terms of its characterization, aryepiglottic vibration is considerably irregular, but it shows indications of contributing quasi- harmonic excitation of the vocal tract, particularly noticeable under conditions of glottal voicelessness. Aryepiglottic vibrations appear to be largely independent of glottal vibration in terms of oscillatory frequency but can be increased in frequency by increasing overall laryngeal constriction. There is evidence that aryepiglottic vibration induces an alternating vocal fold vibration pattern. It is concluded that aryepiglottic trilling, like ventricular phonation, should be regarded as a complex, if highly irregular, sound source

The most extreme Bézier curves.

<p>These are the Bézier curves generated by our method for the most extreme possible values of the four parameters (namely 0.0 and 1.0).</p

Inter-tracing reliability.

<p>The ID is the unique identifier of a HPP. The pairs of tracings are denoted as 1–2, 2–3 and 1–3, respectively. <i>r</i> is the Pearson’s correlation, <i>d</i>_<i>E</i> the Euclidean distance, <i>d</i>_<i>P</i> the (ordinary) Procrustes distance between pairs of tracings, and <i>d</i>_<i>Pgen</i> the generalized Procrustes distance between all three replication tracings simultaneously. For 100 discretization steps, each between 0.0 and 1.0, the maximum possible Euclidean distance is . The bottom part of the table (<i>italic</i>, below the line) gives the means across HPPs.</p

The mean and standard deviation of the goodness of fit per condition.

<p>The mean and standard deviation of the goodness of fit per condition.</p

Original tracings per hard palate profile.

<p>The three independent original replication tracings per HPP are shown with different colors. The tracings are oriented with the alveolar ridge to the right. The <i>x</i> and <i>y</i> coordinates have been mirrored to respect the conventions in this paper and are scaled respecting the original x/y scale. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0191557#pone.0191557.g011" target="_blank">Fig 11</a> shows the normalized tracings allowing a better view of how the shape varies across the traces.</p

Comparing the goodness of fit across conditions.

<p>The distribution of goodness of fit (<i>MSE</i>) across conditions (identified both on the horizontal axis and by color) represented as boxplots.</p