Analyzing phonetic variation in the traditional English dialects: Simultaneously clustering dialects and phonetic features

Wieling, Martijn; Shackleton, Robert G.; Nerbonne, John

doi:10.1093/llc/fqs047

Cited by 6 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The underlying analysis thus often ignores the spatial nature of the input data. Examples include unsupervised clustering (Wieling et al, 2013;Scherrer and Stoeckle, 2016), principal component analysis (Hyvönen et al, 2007;Pröll et al, 2014), multidimensional scaling (Nerbonne, 2009), and regression analysis (Jeszenszky et al, 2017). There are exceptions to this rule with, for instance, Wieling et al (2011) using generalized additive models and splines to fit spatial trend surfaces.…”

Section: Incorporating Spatial Dependencementioning

confidence: 99%

Dialect borders—political regions are better predictors than economy or religion

Derungs

Sieber²,

Glaser

et al. 2019

Digital Scholarship in the Humanities

View full text Add to dashboard Cite

The impact of geography on language and dialect variation has been subject to a vast number of studies in linguistics, as well as the broader humanities. Most quantitative research concerning dialectology has focused on the relationship between spatial distance and change of dialects. Hypotheses regarding the impact of other geographic, cultural, and social factors have been put forth for many years but have rarely been examined with quantitative methods. In this article, we compare dialect variation in Swiss German morphosyntax with three types of social and cultural variation, namely variation in religion, administration, and economy. These different types of variation have contrasting temporal origins. Religion is, for instance, represented by the borders between Christian denominations, which are a result of the Reformation in the 16th century. In order to compare different types of spatial information in one statistical model, we introduce an approach that is robust for spatial dependencies. On one hand, our results are largely in agreement with previous studies. Spatial distance, for instance, proves to be the most important predictor of dialect variance, with distance measures that more realistically represent the potential for social contact, explaining a higher proportion of variance. On the other hand, most interestingly, we find evidence that administrative borders (i.e. political regions) more profoundly impact Swiss dialects than religion or economy. This opens the floor for the hypothesis that possibly both Swiss dialects and political regions have common origins in ancient migration movements and medieval borders between Alemannic territories.

show abstract

Section: Incorporating Spatial Dependencementioning

confidence: 99%

Dialect borders—political regions are better predictors than economy or religion

Derungs

Sieber²,

Glaser

et al. 2019

Digital Scholarship in the Humanities

View full text Add to dashboard Cite

show abstract

“…It would be vain to try to establish, on the basis of a Swadesh list of 176 terms (24 words were excludedsee Supplementary Materials, Tab S1), the regular connections between languages of the same family. In this section, our purpose is to highlight the phonetical similarity and differences of different Central Asian varieties to suggest that their diversity falls in a range of diversity comparable to the European dialects we have studied so far (Gooskens and Heeringa 2004;Nerbonne and Siedle 2005;Wieling et al 2007;Prokić et al 2009;Wieling et al , 2013;Šimičić et al 2013;Montemagni et al 2013). As a consequence the computational methods we used to measure the linguistic diversity, originally designed to analyze dialect diversity in Europe, can be regarded as appropriate tools for the task at hand.…”

Section: General Sketch Of Phonetical Variabilitymentioning

confidence: 99%

“…Methodologically we have the modest aim of using a measure of pronunciation (dis)similarity as an inverse measure of relatedness within the two genealogical families. We measure pronunciation dissimilarity using Levenshtein distance, which has seen a great deal of use in dialectology (Wieling and Nerbonne 2015), but less in assaying relations at a great historical depth.…”

Section: Introductionmentioning

confidence: 99%

A Central Asian Language Survey

Mennecier¹,

Nerbonne

Heyer³

et al. 2016

Lang Dyn Change

View full text Add to dashboard Cite

We have documented language varieties (either Turkic or Indo-European) spoken in 23 test sites by 88 informants belonging to the major ethnic groups of Kyrgyzstan, Tajikistan and Uzbekistan (Karakalpaks, Kazakhs, Kyrgyz, Tajiks, Uzbeks, Yaghnobis). The recorded linguistic material concerns 176 words of the extended Swadesh list and will be made publically available with the publication of this paper. Phonological diversity is measured by the Levenshtein distance and displayed as a consensus bootstrap tree and as multidimensional scaling plots. Linguistic contact is measured as the number of borrowings, from one linguistic family into the other, according to a precision/recall analysis further validated by expert judgment. Concerning Turkic languages, the results of our sample do not support Kazakh and Karakalpak as distinct languages and indicate the existence of several separate Karakalpak varieties. Kyrgyz and Uzbek, on the other hand, appear quite homogeneous. Among the Indo-Iranian languages, the distinction between Tajik and Yaghnobi varieties is very clear-cut. More generally, the degree of borrowing is higher than average where language families are in contact in one of the many sorts of situations characterizing Central Asia: frequent bilingualism, shifting political boundaries, ethnic groups living outside the “mother” country.

show abstract

“…Dialectometry (Séguy, 1973;Goebl, 1982) aims at providing an objective view of dialect variation through the use of quantitative data analysis. In particular, dialectometric clustering has been applied to several regions, including the Netherlands (Wieling & Nerbonne, 2011), Catalonia (Valls et al 2012), and English dialects (Wieling, Shackleton, & Nerbonne, 2013). In Italy, relevant examples mostly concern Tuscany (Montemagni & Wieling, 2016;Calamai, Piccardi, & Nodari, 2022).…”

Section: Introductionmentioning

confidence: 99%

Dialectometry-based classification of the Central–Southern Italian dialects

Sciarretta

2024

J. of Ling. Geography

View full text Add to dashboard Cite

This paper provides a new classification of Central–Southern Italian dialects using dialectometric methods. All varieties considered are analyzed and cast in a data set where homogeneous areas are evaluated according to a selected list of phonetic features. Using numerical evaluation of these features and the Manhattan distance, a linguistic distance rule is defined. On this basis, the classification problem is formulated as a clustering problem, and a k-means algorithm is used. Additionally, an ad-hoc rule is set to identify transitional areas, and silhouette analysis is used to select the most appropriate number of clusters. While meaningful results are obtained for each number of clusters, a nine-group classification emerges as the most appropriate. As the results suggest, this classification is less subjective, more precise, and more comprehensive than traditional ones based on selected isoglosses.

show abstract

Analyzing phonetic variation in the traditional English dialects: Simultaneously clustering dialects and phonetic features

Cited by 6 publications

References 6 publications

Dialect borders—political regions are better predictors than economy or religion

Dialect borders—political regions are better predictors than economy or religion

A Central Asian Language Survey

Dialectometry-based classification of the Central–Southern Italian dialects

Contact Info

Product

Resources

About