Quantitative identification of dialect-specific articulatory settings

Abstract

The purpose of this study was to quantitatively contrast the articulatory settings of two Dutch dialects. Tongue movement data during speech were collected on site at two high schools (34 speakers) in the Netherlands using a portable electromagnetic articulography device. Comparing the tongue positions during pauses in speech between the two groups revealed a clear difference in the articulatory settings, with significantly more frontal tongue positions for the speakers from Ubbergen in the Southeast of the Netherlands compared to those from Ter Apel in the North of the Netherlands. These results provide quantitative evidence for differences in articulatory settings at the dialect level.

Native Language Influence on Brass Instrument Performance: An Application of Generalized Additive Mixed Models (GAMMs) to Midsagittal Ultrasound Images of the Tongue

“…The validity of the concept was first experimentally verified by Gick et al (2004) using old x-ray data; the authors found that interspeech postures (ISPs) “assumed between speech utterances: (a) are language-specific; (b) function as active targets; (c) are active during speech, corresponding with the notion of ASs [articulatory settings], and (d) exert measurable influences on speech targets, most notably including effects on the properties of neutral vowels such as schwa” (p. 231). These findings have since been replicated across languages (Wilson et al, 2007; Wilson and Gick, 2014) and dialects (Wieling and Tiede, 2017), and Ramanarayanan et al (2013) were able to show that ISPs also differ across speech styles (read vs. spontaneous speech) using real-time MRI.…”

“…Note, however, that ISPs and IPPs are much harder to measure than vowels since either occur much less frequently, and the latter is even more so the case for IPPs due to the frequent occurrence of deep in-breaths during rests from playing, which require a very open vocal tract. Ultimately, it may not be necessary to measure AS/ISPs (and IPPs) separately, as suggested by an observation from Wieling and Tiede (2017) where they compare findings on ISPs across Dutch dialects to their earlier findings on tongue movements during word pronunciation (Wieling et al, 2016) within the same data set; they found that for both vowels and ISPs, one dialect group featured a more posterior tongue position than the other (measured using EMA), concluding that “articulatory setting differences may also be observed when analyzing a sizeable amount of variable speech data (i.e., not only focusing on a single segment)” (Wieling and Tiede, 2017, p. 392).…”

“…Generalized Additive Mixed-effects Models (GAMMs) represent a statistical technique that deals with non-linear relationships between time-varying predictors and outcome variables (Hastie and Tibshirani, 1986; Wood, 2006, 2017). The technique has received attention within the phonetics community recently with major publications featuring the use of GAMMs to quantify the dynamics of formant trajectories over time from an acoustic point of view (Sóskuthy, 2017), or tongue position changes measured over time (Wieling et al, 2016; Wieling and Tiede, 2017). GAMMs work by applying a smoothing function (henceforth simply ‘smooth’) to a time-series that can be adjusted to the specific variables that may influence it.…”

Rapid movements at segment boundaries

“…In addition, a likelihood ratio test was performed to test whether adding the complexity of random slope was warranted. The complexities were added when the model comparison showed that the Akaike information criterion (AIC) value of the more complicated model was lower than the value of the less complicated model by at least two (following Wieling and Tiede, 2017). The linear mixed effects models were, thus, established as [time lag] $ place of articulation þ (place of articulation R j speaker) ( R denotes when random slope was added to the model; see Table IV).…”